{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f3320239",
   "metadata": {},
   "source": [
    "# Faster R-CNN on Coco\n",
    "# 🚀 Faster R-CNN：目标检测的里程碑之作\n",
    "\n",
    "Faster R-CNN 是目标检测领域里程碑式的工作，由 Shaoqing Ren、Kaiming He、Ross Girshick 和 Jian Sun 在 2015 年提出（论文标题：[*Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks*](https://arxiv.org/abs/1506.01497)）。它在 R-CNN 系列中承前启后，解决了此前方法的速度瓶颈，同时保持了高精度。\n",
    "\n",
    "---\n",
    "\n",
    "## 🌟 一、核心创新点\n",
    "\n",
    "### 1. 引入 Region Proposal Network (RPN) —— 革命性的区域提议机制\n",
    "\n",
    "- **取代 Selective Search**：在 Fast R-CNN 中，区域提议仍依赖外部算法（如 Selective Search），速度慢且与网络分离。\n",
    "- **RPN 是全卷积网络**：与检测网络共享卷积特征图，直接在特征图上生成候选框（anchor-based），实现“端到端”训练。\n",
    "- **多尺度 anchor 设计**：在每个位置预设多个尺度和长宽比的 anchor，有效应对不同大小目标。\n",
    "\n",
    "### 2. 端到端联合训练\n",
    "\n",
    "- RPN 和 Fast R-CNN 检测头共享卷积特征，通过交替训练或近似联合训练，实现整个网络的端到端优化。\n",
    "- 这是首次将“区域提议 + 目标检测”统一到一个深度网络中，极大提升效率和一致性。\n",
    "\n",
    "### 3. Anchor 机制的开创性应用\n",
    "\n",
    "- Faster R-CNN 首次系统化使用 anchor boxes 作为候选框基础，这一思想深刻影响了后续几乎所有目标检测器（如 SSD、YOLOv2+、RetinaNet 等）。\n",
    "- Anchor 机制让网络学习“相对偏移”，而非绝对坐标，提升泛化能力。\n",
    "\n",
    "### 4. 计算效率大幅提升\n",
    "\n",
    "- 相比 R-CNN（~50s/图）和 Fast R-CNN（~2s/图），Faster R-CNN 在 GPU 上可达 **5–10 FPS**（使用 VGG16），真正迈向“近实时”检测。\n",
    "- 共享卷积特征避免重复计算，是效率飞跃的关键。\n",
    "\n",
    "---\n",
    "\n",
    "## 🌍 二、影响力与历史地位\n",
    "\n",
    "### 1. 奠定 Two-Stage 检测器的主流架构\n",
    "\n",
    "- 成为后续如 **Mask R-CNN**、**Cascade R-CNN**、**Libra R-CNN** 等高性能检测器的基础框架。\n",
    "- “RPN + RoI Pooling + 分类/回归头” 成为标准范式。\n",
    "\n",
    "### 2. 推动目标检测进入深度学习黄金时代\n",
    "\n",
    "- 在 **PASCAL VOC**、**MS COCO** 等权威数据集上长期霸榜，证明深度学习在检测任务上的强大能力。\n",
    "- 2015 年 COCO 检测冠军即基于 Faster R-CNN，引发学术界和工业界广泛关注。\n",
    "\n",
    "### 3. 工业落地广泛\n",
    "\n",
    "- 被广泛应用于 **自动驾驶**（如 Waymo）、**机器人视觉**、**安防监控**、**医疗影像**等领域。\n",
    "- 成为许多商业检测系统的底层架构。\n",
    "\n",
    "### 4. 启发 One-Stage 方法的发展\n",
    "\n",
    "- 尽管是 Two-Stage 方法，但其 **anchor 机制** 被 SSD、YOLOv2 等 One-Stage 方法借鉴，推动整个领域演进。\n",
    "- 后续很多改进（如 FPN、ATSS、IoU Loss）都建立在 Faster R-CNN 框架之上。\n",
    "\n",
    "### 5. 学术引用与开源生态\n",
    "\n",
    "- 截至 2024 年，论文引用量 **超 4 万次**（Google Scholar），是计算机视觉领域被引最高的论文之一。\n",
    "- 官方代码和众多复现版本（如 **Detectron**、**mmdetection**）极大推动社区发展。\n",
    "\n",
    "---\n",
    "\n",
    "## ✅ 总结一句话\n",
    "\n",
    "> **Faster R-CNN 通过引入 RPN 和 anchor 机制，首次实现端到端可训练的高效目标检测框架，不仅大幅提速，更奠定了现代目标检测的基础架构，是深度学习目标检测发展史上的分水岭之作。**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1e2f4d9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use device:  cuda\n"
     ]
    }
   ],
   "source": [
    "# 自动重新加载外部module，使得修改代码之后无需重新import\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from hdd.device.utils import get_device\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "from pathlib import Path\n",
    "\n",
    "# 设置训练数据的路径\n",
    "DATA_ROOT = Path(\"~/workspace/hands-dirty-on-dl/dataset\").expanduser()\n",
    "# 设置预训练模型参数路径\n",
    "TORCH_HUB_PATH = Path(\"~/workspace/hands-dirty-on-dl/pretrained_models\").expanduser()\n",
    "torch.hub.set_dir(TORCH_HUB_PATH)\n",
    "# 挑选最合适的训练设备\n",
    "DEVICE = get_device([\"cuda\", \"cpu\"])\n",
    "print(\"Use device: \", DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2df4e30f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading annotations into memory...\n",
      "Done (t=5.97s)\n",
      "creating index...\n",
      "index created!\n",
      "loading annotations into memory...\n",
      "Done (t=0.93s)\n",
      "creating index...\n",
      "index created!\n",
      "Train dataset size: 117266\n",
      "Val dataset size: 5000\n",
      "----------------------------------------------------\n",
      "type(img) = <class 'torch.Tensor'>\n",
      "type(target) = <class 'dict'>\n",
      "target.keys() = dict_keys(['image_id', 'boxes', 'labels'])\n",
      "type(target['boxes']) = <class 'torch.Tensor'>\n",
      "type(target['labels']) = <class 'torch.Tensor'>\n"
     ]
    }
   ],
   "source": [
    "from torchvision import models, datasets, tv_tensors\n",
    "from torchvision.transforms import v2\n",
    "from torchvision.datasets import wrap_dataset_for_transforms_v2\n",
    "from hdd.scripts.detection.coco_utils import _coco_remove_images_without_annotations\n",
    "\n",
    "COCO_ROOT = Path(DATA_ROOT).expanduser() / \"coco\"\n",
    "\n",
    "\n",
    "def get_coco(train: bool, transforms=None):\n",
    "    image_path = f\"{COCO_ROOT}/train2017\" if train else f\"{COCO_ROOT}/val2017\"\n",
    "    annotation_file_path = (\n",
    "        f\"{COCO_ROOT}/annotations/instances_train2017.json\"\n",
    "        if train\n",
    "        else f\"{COCO_ROOT}/annotations/instances_val2017.json\"\n",
    "    )\n",
    "\n",
    "    dataset = datasets.CocoDetection(\n",
    "        root=image_path,\n",
    "        annFile=annotation_file_path,\n",
    "        transforms=transforms,\n",
    "    )\n",
    "    target_keys = [\"boxes\", \"labels\", \"image_id\"]\n",
    "    dataset = wrap_dataset_for_transforms_v2(dataset, target_keys=target_keys)\n",
    "\n",
    "    if train:\n",
    "        dataset = _coco_remove_images_without_annotations(dataset)\n",
    "    return dataset\n",
    "\n",
    "\n",
    "train_transforms = v2.Compose(\n",
    "    [\n",
    "        v2.ToImage(),\n",
    "        v2.RandomHorizontalFlip(p=0.5),\n",
    "        v2.ToDtype(torch.float32, scale=True),\n",
    "        v2.ConvertBoundingBoxFormat(tv_tensors.BoundingBoxFormat.XYXY),\n",
    "        v2.SanitizeBoundingBoxes(),\n",
    "        v2.ToPureTensor(),\n",
    "    ]\n",
    ")\n",
    "\n",
    "val_transforms = v2.Compose(\n",
    "    [\n",
    "        v2.ToImage(),\n",
    "        v2.ToDtype(torch.float32, scale=True),\n",
    "        v2.ConvertBoundingBoxFormat(tv_tensors.BoundingBoxFormat.XYXY),\n",
    "        v2.ToPureTensor(),\n",
    "    ]\n",
    ")\n",
    "\n",
    "train_dataset = get_coco(train=True, transforms=train_transforms)\n",
    "val_dataset = get_coco(train=False, transforms=val_transforms)\n",
    "print(f\"Train dataset size: {len(train_dataset)}\")\n",
    "print(f\"Val dataset size: {len(val_dataset)}\")\n",
    "print(\"----------------------------------------------------\")\n",
    "img, target = val_dataset[0]\n",
    "print(f\"{type(img) = }\\n{type(target) = }\\n{target.keys() = }\")\n",
    "print(f\"{type(target['boxes']) = }\\n{type(target['labels']) = }\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "db7cbc27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using [0, 0.5, 0.6299605249474366, 0.7937005259840997, 1.0, 1.2599210498948732, 1.5874010519681994, 2.0, inf] as bins for aspect ratio quantization\n",
      "Count of instances per bin: [  104   982 24236  2332  8225 74466  5763  1158]\n"
     ]
    }
   ],
   "source": [
    "from hdd.scripts.detection.group_by_aspect_ratio import (\n",
    "    GroupedBatchSampler,\n",
    "    create_aspect_ratio_groups,\n",
    ")\n",
    "\n",
    "train_batch_size = 16\n",
    "train_sampler = torch.utils.data.RandomSampler(train_dataset)\n",
    "group_ids = create_aspect_ratio_groups(train_dataset, k=3)\n",
    "train_batch_sampler = GroupedBatchSampler(train_sampler, group_ids, train_batch_size)\n",
    "val_sampler = torch.utils.data.SequentialSampler(val_transforms)\n",
    "train_dataloader = torch.utils.data.DataLoader(\n",
    "    train_dataset,\n",
    "    batch_size=train_batch_size,\n",
    "    # We need a custom collation function here, since the object detection\n",
    "    # models expect a sequence of images and target dictionaries. The default\n",
    "    # collation function tries to torch.stack() the individual elements,\n",
    "    # which fails in general for object detection, because the number of bounding\n",
    "    # boxes varies between the images of the same batch.\n",
    "    collate_fn=lambda batch: tuple(zip(*batch)),\n",
    "    shuffle=True,\n",
    "    num_workers=8,\n",
    ")\n",
    "\n",
    "val_dataloader = torch.utils.data.DataLoader(\n",
    "    val_dataset,\n",
    "    batch_size=train_batch_size,\n",
    "    collate_fn=lambda batch: tuple(zip(*batch)),\n",
    "    num_workers=4,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14005373",
   "metadata": {},
   "source": [
    "### Modifying the model to add a different backbone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1bc60ef6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tf/anaconda3/envs/pytorch-cu124/lib/python3.11/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "/home/tf/anaconda3/envs/pytorch-cu124/lib/python3.11/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MobileNet_V2_Weights.IMAGENET1K_V1`. You can also use `weights=MobileNet_V2_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "import torchvision\n",
    "from torchvision.models.detection import FasterRCNN\n",
    "from torchvision.models.detection.rpn import AnchorGenerator\n",
    "from torchvision.models import MobileNet_V2_Weights\n",
    "from torchvision.models.detection.backbone_utils import mobilenet_backbone\n",
    "\n",
    "# load a pre-trained model for classification and return only the features\n",
    "# mobilenet_backbone会将BN Layer冻结且可指定可训练的层数\n",
    "backbone = mobilenet_backbone(\n",
    "    backbone_name=\"mobilenet_v2\", fpn=False, pretrained=True, trainable_layers=3\n",
    ")\n",
    "\n",
    "# let's make the RPN generate 5 x 3 anchors per spatial\n",
    "# location, with 5 different sizes and 3 different aspect\n",
    "# ratios. We have a Tuple[Tuple[int]] because each feature\n",
    "# map could potentially have different sizes and\n",
    "# aspect ratios\n",
    "anchor_generator = AnchorGenerator(\n",
    "    sizes=((32, 64, 128, 256, 512),), aspect_ratios=((0.5, 1.0, 2.0),)\n",
    ")\n",
    "\n",
    "# let's define what are the feature maps that we will\n",
    "# use to perform the region of interest cropping, as well as\n",
    "# the size of the crop after rescaling.\n",
    "# if your backbone returns a Tensor, featmap_names is expected to\n",
    "# be [0]. More generally, the backbone should return an\n",
    "# ``OrderedDict[Tensor]``, and in ``featmap_names`` you can choose which\n",
    "# feature maps to use.\n",
    "roi_pooler = torchvision.ops.MultiScaleRoIAlign(\n",
    "    featmap_names=[\"0\"], output_size=7, sampling_ratio=2\n",
    ")\n",
    "\n",
    "# put the pieces together inside a Faster-RCNN model\n",
    "num_classes = 91\n",
    "model = FasterRCNN(\n",
    "    backbone,\n",
    "    num_classes=num_classes,\n",
    "    rpn_anchor_generator=anchor_generator,\n",
    "    box_roi_pool=roi_pooler,\n",
    ")\n",
    "model = model.to(DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e0234ffe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hdd.train.warmup_scheduler import GradualWarmupScheduler\n",
    "\n",
    "weight_decay = 1e-4\n",
    "# ！！！！！！！！！！！！！！！！！！！！！！！！！\n",
    "# ！！！！！！！！！！！！！！！！！！！！！！！！！\n",
    "# 调了好久的adamw结果发现learning rate太高了\n",
    "# ！！！！！！！！！！！！！！！！！！！！！！！！！\n",
    "# ！！！！！！！！！！！！！！！！！！！！！！！！！\n",
    "learning_rate = 0.00001\n",
    "max_iterations = 50_000\n",
    "norm_params, other_params = torchvision.ops._utils.split_normalization_params(model)\n",
    "# Do not apply weight decay to norm parameters\n",
    "parameters = [\n",
    "    {\"params\": norm_params, \"weight_decay\": 0},\n",
    "    {\"params\": other_params, \"weight_decay\": weight_decay},\n",
    "]\n",
    "optimizer = torch.optim.AdamW(\n",
    "    parameters, lr=learning_rate, betas=[0.9, 0.99], weight_decay=weight_decay\n",
    ")\n",
    "base_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n",
    "    optimizer, max_iterations, eta_min=1e-6\n",
    ")\n",
    "scheduler = GradualWarmupScheduler(\n",
    "    optimizer,\n",
    "    multiplier=1.0,\n",
    "    total_epoch=2000,\n",
    "    after_scheduler=base_scheduler,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ffce98ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hdd.scripts.detection import utils\n",
    "import math\n",
    "import sys\n",
    "\n",
    "\n",
    "def train_one_epoch(\n",
    "    model, optimizer, scheduler, data_loader, device, epoch, print_freq, scaler=None\n",
    ") -> utils.MetricLogger:\n",
    "    model.train()\n",
    "    metric_logger = utils.MetricLogger(delimiter=\"  \")\n",
    "    metric_logger.add_meter(\"lr\", utils.SmoothedValue(window_size=1, fmt=\"{value:.6f}\"))\n",
    "    header = f\"Epoch: [{epoch}]\"\n",
    "\n",
    "    for images, targets in metric_logger.log_every(data_loader, print_freq, header):\n",
    "        images = list(image.to(device) for image in images)\n",
    "        targets = [\n",
    "            {\n",
    "                k: v.to(device) if isinstance(v, torch.Tensor) else v\n",
    "                for k, v in t.items()\n",
    "            }\n",
    "            for t in targets\n",
    "        ]\n",
    "        with torch.amp.autocast(enabled=scaler is not None, device_type=\"cuda\"):\n",
    "            loss_dict = model(images, targets)\n",
    "            losses = sum(loss for loss in loss_dict.values())\n",
    "\n",
    "        # reduce losses over all GPUs for logging purposes\n",
    "        loss_dict_reduced = utils.reduce_dict(loss_dict)\n",
    "        losses_reduced = sum(loss for loss in loss_dict_reduced.values())\n",
    "\n",
    "        loss_value = losses_reduced.item()\n",
    "\n",
    "        if not math.isfinite(loss_value):\n",
    "            print(f\"Loss is {loss_value}, stopping training\")\n",
    "            print(loss_dict_reduced)\n",
    "            sys.exit(1)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        if scaler is not None:\n",
    "            scaler.scale(losses).backward()\n",
    "            scaler.step(optimizer)\n",
    "            scaler.update()\n",
    "        else:\n",
    "            losses.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "        scheduler.step()\n",
    "\n",
    "        metric_logger.update(loss=losses_reduced, **loss_dict_reduced)\n",
    "        metric_logger.update(lr=optimizer.param_groups[0][\"lr\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a75459ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "from hdd.scripts.detection.coco_utils import get_coco_api_from_dataset\n",
    "from hdd.scripts.detection.coco_eval import CocoEvaluator\n",
    "\n",
    "\n",
    "@torch.inference_mode()\n",
    "def evaluate(model, data_loader, device):\n",
    "    n_threads = torch.get_num_threads()\n",
    "    # FIXME remove this and make paste_masks_in_image run on the GPU\n",
    "    cpu_device = torch.device(\"cpu\")\n",
    "    model.eval()\n",
    "    metric_logger = utils.MetricLogger(delimiter=\"  \")\n",
    "    header = \"Test:\"\n",
    "\n",
    "    coco = get_coco_api_from_dataset(data_loader.dataset)\n",
    "    iou_types = [\"bbox\"]\n",
    "    coco_evaluator = CocoEvaluator(coco, iou_types)\n",
    "\n",
    "    for images, targets in metric_logger.log_every(data_loader, 1000, header):\n",
    "        images = list(img.to(device) for img in images)\n",
    "\n",
    "        if torch.cuda.is_available():\n",
    "            torch.cuda.synchronize()\n",
    "        model_time = time.time()\n",
    "        outputs = model(images)\n",
    "\n",
    "        outputs = [{k: v.to(cpu_device) for k, v in t.items()} for t in outputs]\n",
    "        model_time = time.time() - model_time\n",
    "\n",
    "        res = {target[\"image_id\"]: output for target, output in zip(targets, outputs)}\n",
    "        evaluator_time = time.time()\n",
    "        coco_evaluator.update(res)\n",
    "        evaluator_time = time.time() - evaluator_time\n",
    "        metric_logger.update(model_time=model_time, evaluator_time=evaluator_time)\n",
    "\n",
    "    print(\"Averaged stats:\", metric_logger)\n",
    "\n",
    "    # accumulate predictions from all images\n",
    "    coco_evaluator.accumulate()\n",
    "    coco_evaluator.summarize()\n",
    "    return coco_evaluator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9b885666",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max epochs: 6\n",
      "Epoch: [0]  [   0/7330]  eta: 3:28:01  lr: 0.000000  loss: 5.4267 (5.4267)  loss_classifier: 4.5129 (4.5129)  loss_box_reg: 0.0877 (0.0877)  loss_objectness: 0.6918 (0.6918)  loss_rpn_box_reg: 0.1343 (0.1343)  time: 1.7028  data: 0.4867  max mem: 4694\n",
      "Epoch: [0]  [ 100/7330]  eta: 0:28:38  lr: 0.000001  loss: 5.2504 (5.3277)  loss_classifier: 4.3832 (4.4623)  loss_box_reg: 0.0605 (0.0537)  loss_objectness: 0.6908 (0.6915)  loss_rpn_box_reg: 0.1224 (0.1202)  time: 0.2223  data: 0.0059  max mem: 7728\n",
      "Epoch: [0]  [ 200/7330]  eta: 0:27:27  lr: 0.000001  loss: 2.7674 (4.7979)  loss_classifier: 1.9156 (3.9324)  loss_box_reg: 0.0827 (0.0627)  loss_objectness: 0.6746 (0.6878)  loss_rpn_box_reg: 0.1076 (0.1149)  time: 0.2259  data: 0.0061  max mem: 7728\n",
      "Epoch: [0]  [ 300/7330]  eta: 0:26:41  lr: 0.000002  loss: 1.2722 (3.6590)  loss_classifier: 0.4222 (2.7942)  loss_box_reg: 0.1153 (0.0773)  loss_objectness: 0.6192 (0.6727)  loss_rpn_box_reg: 0.1122 (0.1148)  time: 0.2177  data: 0.0058  max mem: 7729\n",
      "Epoch: [0]  [ 400/7330]  eta: 0:25:51  lr: 0.000002  loss: 1.3597 (3.0802)  loss_classifier: 0.5229 (2.2199)  loss_box_reg: 0.1862 (0.0971)  loss_objectness: 0.5546 (0.6505)  loss_rpn_box_reg: 0.1200 (0.1127)  time: 0.2135  data: 0.0061  max mem: 7729\n",
      "Epoch: [0]  [ 500/7330]  eta: 0:25:08  lr: 0.000003  loss: 1.3058 (2.7190)  loss_classifier: 0.5276 (1.8772)  loss_box_reg: 0.2084 (0.1136)  loss_objectness: 0.4460 (0.6177)  loss_rpn_box_reg: 0.1244 (0.1105)  time: 0.2083  data: 0.0059  max mem: 7729\n",
      "Epoch: [0]  [ 600/7330]  eta: 0:24:30  lr: 0.000003  loss: 1.2262 (2.4646)  loss_classifier: 0.5258 (1.6525)  loss_box_reg: 0.2163 (0.1280)  loss_objectness: 0.3293 (0.5752)  loss_rpn_box_reg: 0.1139 (0.1089)  time: 0.2052  data: 0.0059  max mem: 7729\n",
      "Epoch: [0]  [ 700/7330]  eta: 0:23:55  lr: 0.000004  loss: 1.0497 (2.2778)  loss_classifier: 0.4986 (1.4947)  loss_box_reg: 0.2208 (0.1406)  loss_objectness: 0.2666 (0.5349)  loss_rpn_box_reg: 0.0884 (0.1076)  time: 0.2040  data: 0.0061  max mem: 7729\n",
      "Epoch: [0]  [ 800/7330]  eta: 0:23:24  lr: 0.000004  loss: 1.0777 (2.1342)  loss_classifier: 0.4992 (1.3757)  loss_box_reg: 0.2070 (0.1520)  loss_objectness: 0.2372 (0.5003)  loss_rpn_box_reg: 0.0844 (0.1062)  time: 0.2066  data: 0.0058  max mem: 7729\n",
      "Epoch: [0]  [ 900/7330]  eta: 0:22:55  lr: 0.000005  loss: 0.9255 (2.0114)  loss_classifier: 0.4283 (1.2769)  loss_box_reg: 0.1971 (0.1592)  loss_objectness: 0.2233 (0.4705)  loss_rpn_box_reg: 0.0860 (0.1048)  time: 0.2058  data: 0.0058  max mem: 7729\n",
      "Epoch: [0]  [1000/7330]  eta: 0:22:30  lr: 0.000005  loss: 0.9012 (1.9015)  loss_classifier: 0.4066 (1.1909)  loss_box_reg: 0.1878 (0.1623)  loss_objectness: 0.2024 (0.4447)  loss_rpn_box_reg: 0.0990 (0.1037)  time: 0.2093  data: 0.0058  max mem: 7729\n",
      "Epoch: [0]  [1100/7330]  eta: 0:22:09  lr: 0.000006  loss: 0.7864 (1.8050)  loss_classifier: 0.3479 (1.1175)  loss_box_reg: 0.1763 (0.1637)  loss_objectness: 0.1781 (0.4210)  loss_rpn_box_reg: 0.0798 (0.1027)  time: 0.2175  data: 0.0057  max mem: 7729\n",
      "Epoch: [0]  [1200/7330]  eta: 0:21:48  lr: 0.000006  loss: 0.7698 (1.7241)  loss_classifier: 0.3273 (1.0558)  loss_box_reg: 0.1583 (0.1648)  loss_objectness: 0.1657 (0.4012)  loss_rpn_box_reg: 0.0823 (0.1022)  time: 0.2180  data: 0.0060  max mem: 7729\n",
      "Epoch: [0]  [1300/7330]  eta: 0:21:27  lr: 0.000007  loss: 0.7798 (1.6543)  loss_classifier: 0.3569 (1.0030)  loss_box_reg: 0.1778 (0.1659)  loss_objectness: 0.1666 (0.3835)  loss_rpn_box_reg: 0.0822 (0.1019)  time: 0.2112  data: 0.0058  max mem: 7729\n",
      "Epoch: [0]  [1400/7330]  eta: 0:21:08  lr: 0.000007  loss: 0.8508 (1.5928)  loss_classifier: 0.3798 (0.9571)  loss_box_reg: 0.1852 (0.1670)  loss_objectness: 0.1556 (0.3677)  loss_rpn_box_reg: 0.0920 (0.1010)  time: 0.2171  data: 0.0058  max mem: 7729\n",
      "Epoch: [0]  [1500/7330]  eta: 0:20:47  lr: 0.000008  loss: 0.7979 (1.5393)  loss_classifier: 0.3459 (0.9170)  loss_box_reg: 0.1791 (0.1680)  loss_objectness: 0.1696 (0.3538)  loss_rpn_box_reg: 0.0894 (0.1005)  time: 0.2130  data: 0.0058  max mem: 7731\n",
      "Epoch: [0]  [1600/7330]  eta: 0:20:27  lr: 0.000008  loss: 0.8561 (1.4922)  loss_classifier: 0.3800 (0.8817)  loss_box_reg: 0.1923 (0.1692)  loss_objectness: 0.1644 (0.3414)  loss_rpn_box_reg: 0.0970 (0.0999)  time: 0.2202  data: 0.0057  max mem: 7731\n",
      "Epoch: [0]  [1700/7330]  eta: 0:20:06  lr: 0.000009  loss: 0.7096 (1.4502)  loss_classifier: 0.3373 (0.8504)  loss_box_reg: 0.1899 (0.1704)  loss_objectness: 0.1333 (0.3300)  loss_rpn_box_reg: 0.0921 (0.0994)  time: 0.2182  data: 0.0060  max mem: 7731\n",
      "Epoch: [0]  [1800/7330]  eta: 0:19:45  lr: 0.000009  loss: 0.7899 (1.4135)  loss_classifier: 0.3539 (0.8228)  loss_box_reg: 0.2069 (0.1720)  loss_objectness: 0.1364 (0.3197)  loss_rpn_box_reg: 0.0812 (0.0989)  time: 0.2141  data: 0.0056  max mem: 7731\n",
      "Epoch: [0]  [1900/7330]  eta: 0:19:24  lr: 0.000010  loss: 0.7539 (1.3815)  loss_classifier: 0.3455 (0.7985)  loss_box_reg: 0.1986 (0.1738)  loss_objectness: 0.1315 (0.3105)  loss_rpn_box_reg: 0.0781 (0.0987)  time: 0.2206  data: 0.0057  max mem: 7731\n",
      "Epoch: [0]  [2000/7330]  eta: 0:19:03  lr: 0.000010  loss: 0.7632 (1.3513)  loss_classifier: 0.3278 (0.7759)  loss_box_reg: 0.1965 (0.1753)  loss_objectness: 0.1526 (0.3021)  loss_rpn_box_reg: 0.1001 (0.0981)  time: 0.2103  data: 0.0059  max mem: 7731\n",
      "Epoch: [0]  [2100/7330]  eta: 0:18:42  lr: 0.000010  loss: 0.7578 (1.3241)  loss_classifier: 0.3244 (0.7552)  loss_box_reg: 0.2012 (0.1769)  loss_objectness: 0.1333 (0.2942)  loss_rpn_box_reg: 0.0830 (0.0978)  time: 0.2168  data: 0.0056  max mem: 7731\n",
      "Epoch: [0]  [2200/7330]  eta: 0:18:21  lr: 0.000010  loss: 0.7988 (1.2989)  loss_classifier: 0.3446 (0.7362)  loss_box_reg: 0.2166 (0.1783)  loss_objectness: 0.1314 (0.2870)  loss_rpn_box_reg: 0.0804 (0.0973)  time: 0.2134  data: 0.0056  max mem: 7731\n",
      "Epoch: [0]  [2300/7330]  eta: 0:18:00  lr: 0.000010  loss: 0.7618 (1.2760)  loss_classifier: 0.3248 (0.7187)  loss_box_reg: 0.2064 (0.1799)  loss_objectness: 0.1296 (0.2804)  loss_rpn_box_reg: 0.1011 (0.0970)  time: 0.2193  data: 0.0059  max mem: 7731\n",
      "Epoch: [0]  [2400/7330]  eta: 0:17:39  lr: 0.000010  loss: 0.8161 (1.2547)  loss_classifier: 0.3534 (0.7024)  loss_box_reg: 0.2289 (0.1812)  loss_objectness: 0.1406 (0.2744)  loss_rpn_box_reg: 0.0827 (0.0967)  time: 0.2185  data: 0.0057  max mem: 7731\n",
      "Epoch: [0]  [2500/7330]  eta: 0:17:18  lr: 0.000010  loss: 0.7030 (1.2338)  loss_classifier: 0.3049 (0.6870)  loss_box_reg: 0.1905 (0.1820)  loss_objectness: 0.1130 (0.2685)  loss_rpn_box_reg: 0.0738 (0.0962)  time: 0.2216  data: 0.0056  max mem: 7731\n",
      "Epoch: [0]  [2600/7330]  eta: 0:16:57  lr: 0.000010  loss: 0.7202 (1.2154)  loss_classifier: 0.3191 (0.6732)  loss_box_reg: 0.2094 (0.1834)  loss_objectness: 0.1205 (0.2629)  loss_rpn_box_reg: 0.0809 (0.0959)  time: 0.2168  data: 0.0055  max mem: 7731\n",
      "Epoch: [0]  [2700/7330]  eta: 0:16:36  lr: 0.000010  loss: 0.7425 (1.1978)  loss_classifier: 0.3229 (0.6599)  loss_box_reg: 0.2200 (0.1845)  loss_objectness: 0.1180 (0.2578)  loss_rpn_box_reg: 0.0881 (0.0956)  time: 0.2193  data: 0.0055  max mem: 7731\n",
      "Epoch: [0]  [2800/7330]  eta: 0:16:14  lr: 0.000010  loss: 0.6803 (1.1812)  loss_classifier: 0.2892 (0.6474)  loss_box_reg: 0.1925 (0.1854)  loss_objectness: 0.1220 (0.2531)  loss_rpn_box_reg: 0.0873 (0.0953)  time: 0.2156  data: 0.0055  max mem: 7731\n",
      "Epoch: [0]  [2900/7330]  eta: 0:15:53  lr: 0.000010  loss: 0.6689 (1.1662)  loss_classifier: 0.2786 (0.6360)  loss_box_reg: 0.1886 (0.1865)  loss_objectness: 0.1161 (0.2486)  loss_rpn_box_reg: 0.0727 (0.0951)  time: 0.2193  data: 0.0056  max mem: 7731\n",
      "Epoch: [0]  [3000/7330]  eta: 0:15:32  lr: 0.000010  loss: 0.7089 (1.1519)  loss_classifier: 0.2952 (0.6252)  loss_box_reg: 0.2155 (0.1875)  loss_objectness: 0.1058 (0.2443)  loss_rpn_box_reg: 0.0833 (0.0949)  time: 0.2206  data: 0.0055  max mem: 7731\n",
      "Epoch: [0]  [3100/7330]  eta: 0:15:11  lr: 0.000010  loss: 0.7539 (1.1384)  loss_classifier: 0.3144 (0.6150)  loss_box_reg: 0.2228 (0.1885)  loss_objectness: 0.1143 (0.2403)  loss_rpn_box_reg: 0.0946 (0.0946)  time: 0.2170  data: 0.0055  max mem: 7731\n",
      "Epoch: [0]  [3200/7330]  eta: 0:14:49  lr: 0.000010  loss: 0.7423 (1.1261)  loss_classifier: 0.3083 (0.6056)  loss_box_reg: 0.2208 (0.1896)  loss_objectness: 0.1106 (0.2365)  loss_rpn_box_reg: 0.0692 (0.0943)  time: 0.2157  data: 0.0054  max mem: 7731\n",
      "Epoch: [0]  [3300/7330]  eta: 0:14:28  lr: 0.000010  loss: 0.6962 (1.1140)  loss_classifier: 0.2973 (0.5965)  loss_box_reg: 0.2152 (0.1906)  loss_objectness: 0.1222 (0.2329)  loss_rpn_box_reg: 0.0742 (0.0940)  time: 0.2190  data: 0.0055  max mem: 7731\n",
      "Epoch: [0]  [3400/7330]  eta: 0:14:06  lr: 0.000010  loss: 0.6977 (1.1027)  loss_classifier: 0.2832 (0.5880)  loss_box_reg: 0.2078 (0.1915)  loss_objectness: 0.1112 (0.2295)  loss_rpn_box_reg: 0.0857 (0.0937)  time: 0.2140  data: 0.0056  max mem: 7731\n",
      "Epoch: [0]  [3500/7330]  eta: 0:13:45  lr: 0.000010  loss: 0.7015 (1.0923)  loss_classifier: 0.3017 (0.5801)  loss_box_reg: 0.2158 (0.1925)  loss_objectness: 0.1097 (0.2264)  loss_rpn_box_reg: 0.0804 (0.0934)  time: 0.2153  data: 0.0055  max mem: 7731\n",
      "Epoch: [0]  [3600/7330]  eta: 0:13:23  lr: 0.000010  loss: 0.7228 (1.0825)  loss_classifier: 0.3064 (0.5725)  loss_box_reg: 0.2185 (0.1935)  loss_objectness: 0.1190 (0.2233)  loss_rpn_box_reg: 0.0804 (0.0932)  time: 0.2187  data: 0.0056  max mem: 7731\n",
      "Epoch: [0]  [3700/7330]  eta: 0:13:02  lr: 0.000010  loss: 0.6743 (1.0726)  loss_classifier: 0.2809 (0.5651)  loss_box_reg: 0.1987 (0.1942)  loss_objectness: 0.1085 (0.2203)  loss_rpn_box_reg: 0.0870 (0.0930)  time: 0.2202  data: 0.0055  max mem: 7731\n",
      "Epoch: [0]  [3800/7330]  eta: 0:12:40  lr: 0.000010  loss: 0.7329 (1.0635)  loss_classifier: 0.3098 (0.5582)  loss_box_reg: 0.2314 (0.1951)  loss_objectness: 0.1205 (0.2175)  loss_rpn_box_reg: 0.0895 (0.0928)  time: 0.2193  data: 0.0056  max mem: 7731\n",
      "Epoch: [0]  [3900/7330]  eta: 0:12:19  lr: 0.000010  loss: 0.6903 (1.0551)  loss_classifier: 0.2847 (0.5518)  loss_box_reg: 0.2186 (0.1960)  loss_objectness: 0.1107 (0.2148)  loss_rpn_box_reg: 0.0745 (0.0926)  time: 0.2183  data: 0.0055  max mem: 7731\n",
      "Epoch: [0]  [4000/7330]  eta: 0:11:58  lr: 0.000010  loss: 0.7331 (1.0471)  loss_classifier: 0.3009 (0.5457)  loss_box_reg: 0.2297 (0.1968)  loss_objectness: 0.1087 (0.2123)  loss_rpn_box_reg: 0.0773 (0.0924)  time: 0.2175  data: 0.0057  max mem: 7731\n",
      "Epoch: [0]  [4100/7330]  eta: 0:11:36  lr: 0.000010  loss: 0.7167 (1.0393)  loss_classifier: 0.3022 (0.5397)  loss_box_reg: 0.2289 (0.1975)  loss_objectness: 0.1015 (0.2099)  loss_rpn_box_reg: 0.0753 (0.0922)  time: 0.2205  data: 0.0057  max mem: 7731\n",
      "Epoch: [0]  [4200/7330]  eta: 0:11:15  lr: 0.000010  loss: 0.7196 (1.0321)  loss_classifier: 0.2896 (0.5341)  loss_box_reg: 0.2209 (0.1984)  loss_objectness: 0.1132 (0.2076)  loss_rpn_box_reg: 0.0801 (0.0921)  time: 0.2176  data: 0.0055  max mem: 7731\n",
      "Epoch: [0]  [4300/7330]  eta: 0:10:53  lr: 0.000010  loss: 0.7020 (1.0249)  loss_classifier: 0.3076 (0.5287)  loss_box_reg: 0.2350 (0.1991)  loss_objectness: 0.0996 (0.2053)  loss_rpn_box_reg: 0.0764 (0.0918)  time: 0.2165  data: 0.0055  max mem: 7731\n",
      "Epoch: [0]  [4400/7330]  eta: 0:10:32  lr: 0.000010  loss: 0.7119 (1.0184)  loss_classifier: 0.2899 (0.5236)  loss_box_reg: 0.2192 (0.1998)  loss_objectness: 0.1092 (0.2032)  loss_rpn_box_reg: 0.0822 (0.0917)  time: 0.2180  data: 0.0060  max mem: 7731\n",
      "Epoch: [0]  [4500/7330]  eta: 0:10:10  lr: 0.000010  loss: 0.7020 (1.0116)  loss_classifier: 0.2746 (0.5186)  loss_box_reg: 0.2343 (0.2005)  loss_objectness: 0.1050 (0.2011)  loss_rpn_box_reg: 0.0725 (0.0915)  time: 0.2155  data: 0.0057  max mem: 7731\n",
      "Epoch: [0]  [4600/7330]  eta: 0:09:49  lr: 0.000010  loss: 0.7023 (1.0054)  loss_classifier: 0.2866 (0.5139)  loss_box_reg: 0.2318 (0.2012)  loss_objectness: 0.1003 (0.1990)  loss_rpn_box_reg: 0.0814 (0.0913)  time: 0.2160  data: 0.0057  max mem: 7731\n",
      "Epoch: [0]  [4700/7330]  eta: 0:09:27  lr: 0.000010  loss: 0.6776 (0.9992)  loss_classifier: 0.2834 (0.5092)  loss_box_reg: 0.2062 (0.2017)  loss_objectness: 0.1074 (0.1971)  loss_rpn_box_reg: 0.0889 (0.0912)  time: 0.2197  data: 0.0054  max mem: 7731\n",
      "Epoch: [0]  [4800/7330]  eta: 0:09:06  lr: 0.000010  loss: 0.7008 (0.9934)  loss_classifier: 0.2938 (0.5048)  loss_box_reg: 0.2314 (0.2024)  loss_objectness: 0.1018 (0.1952)  loss_rpn_box_reg: 0.0736 (0.0909)  time: 0.2220  data: 0.0059  max mem: 7731\n",
      "Epoch: [0]  [4900/7330]  eta: 0:08:44  lr: 0.000010  loss: 0.7093 (0.9874)  loss_classifier: 0.2938 (0.5004)  loss_box_reg: 0.2200 (0.2029)  loss_objectness: 0.1009 (0.1934)  loss_rpn_box_reg: 0.0755 (0.0907)  time: 0.2165  data: 0.0055  max mem: 7731\n",
      "Epoch: [0]  [5000/7330]  eta: 0:08:23  lr: 0.000010  loss: 0.6850 (0.9822)  loss_classifier: 0.2788 (0.4964)  loss_box_reg: 0.2278 (0.2036)  loss_objectness: 0.1002 (0.1917)  loss_rpn_box_reg: 0.0799 (0.0906)  time: 0.2171  data: 0.0055  max mem: 7731\n",
      "Epoch: [0]  [5100/7330]  eta: 0:08:01  lr: 0.000010  loss: 0.7115 (0.9767)  loss_classifier: 0.2872 (0.4922)  loss_box_reg: 0.2276 (0.2040)  loss_objectness: 0.0933 (0.1900)  loss_rpn_box_reg: 0.0782 (0.0904)  time: 0.2192  data: 0.0055  max mem: 7731\n",
      "Epoch: [0]  [5200/7330]  eta: 0:07:39  lr: 0.000010  loss: 0.6932 (0.9715)  loss_classifier: 0.2915 (0.4884)  loss_box_reg: 0.2359 (0.2046)  loss_objectness: 0.0951 (0.1883)  loss_rpn_box_reg: 0.0740 (0.0902)  time: 0.2050  data: 0.0058  max mem: 7731\n",
      "Epoch: [0]  [5300/7330]  eta: 0:07:18  lr: 0.000010  loss: 0.6638 (0.9667)  loss_classifier: 0.2804 (0.4848)  loss_box_reg: 0.2083 (0.2051)  loss_objectness: 0.0926 (0.1868)  loss_rpn_box_reg: 0.0686 (0.0900)  time: 0.2186  data: 0.0055  max mem: 7731\n",
      "Epoch: [0]  [5400/7330]  eta: 0:06:56  lr: 0.000010  loss: 0.7019 (0.9617)  loss_classifier: 0.2847 (0.4811)  loss_box_reg: 0.2275 (0.2055)  loss_objectness: 0.1006 (0.1852)  loss_rpn_box_reg: 0.0820 (0.0898)  time: 0.2194  data: 0.0058  max mem: 7731\n",
      "Epoch: [0]  [5500/7330]  eta: 0:06:35  lr: 0.000010  loss: 0.7351 (0.9575)  loss_classifier: 0.3004 (0.4778)  loss_box_reg: 0.2354 (0.2061)  loss_objectness: 0.1121 (0.1838)  loss_rpn_box_reg: 0.0827 (0.0898)  time: 0.2154  data: 0.0057  max mem: 7731\n",
      "Epoch: [0]  [5600/7330]  eta: 0:06:13  lr: 0.000010  loss: 0.7340 (0.9530)  loss_classifier: 0.2855 (0.4745)  loss_box_reg: 0.2297 (0.2065)  loss_objectness: 0.1086 (0.1824)  loss_rpn_box_reg: 0.0750 (0.0897)  time: 0.2186  data: 0.0057  max mem: 7731\n",
      "Epoch: [0]  [5700/7330]  eta: 0:05:52  lr: 0.000010  loss: 0.6356 (0.9485)  loss_classifier: 0.2650 (0.4711)  loss_box_reg: 0.2124 (0.2069)  loss_objectness: 0.0936 (0.1810)  loss_rpn_box_reg: 0.0664 (0.0895)  time: 0.2160  data: 0.0057  max mem: 7732\n",
      "Epoch: [0]  [5800/7330]  eta: 0:05:30  lr: 0.000010  loss: 0.6713 (0.9442)  loss_classifier: 0.2614 (0.4679)  loss_box_reg: 0.2273 (0.2073)  loss_objectness: 0.1021 (0.1796)  loss_rpn_box_reg: 0.0707 (0.0894)  time: 0.2176  data: 0.0057  max mem: 7732\n",
      "Epoch: [0]  [5900/7330]  eta: 0:05:09  lr: 0.000010  loss: 0.7199 (0.9403)  loss_classifier: 0.2876 (0.4649)  loss_box_reg: 0.2371 (0.2078)  loss_objectness: 0.0975 (0.1783)  loss_rpn_box_reg: 0.0803 (0.0892)  time: 0.2211  data: 0.0057  max mem: 7732\n",
      "Epoch: [0]  [6000/7330]  eta: 0:04:47  lr: 0.000010  loss: 0.6746 (0.9366)  loss_classifier: 0.2681 (0.4621)  loss_box_reg: 0.2182 (0.2084)  loss_objectness: 0.1021 (0.1770)  loss_rpn_box_reg: 0.0736 (0.0891)  time: 0.2186  data: 0.0057  max mem: 7732\n",
      "Epoch: [0]  [6100/7330]  eta: 0:04:25  lr: 0.000010  loss: 0.7081 (0.9329)  loss_classifier: 0.2667 (0.4592)  loss_box_reg: 0.2240 (0.2089)  loss_objectness: 0.1008 (0.1758)  loss_rpn_box_reg: 0.0833 (0.0891)  time: 0.2193  data: 0.0059  max mem: 7732\n",
      "Epoch: [0]  [6200/7330]  eta: 0:04:04  lr: 0.000010  loss: 0.7378 (0.9294)  loss_classifier: 0.2800 (0.4565)  loss_box_reg: 0.2500 (0.2094)  loss_objectness: 0.1064 (0.1745)  loss_rpn_box_reg: 0.0795 (0.0889)  time: 0.2195  data: 0.0059  max mem: 7732\n",
      "Epoch: [0]  [6300/7330]  eta: 0:03:42  lr: 0.000010  loss: 0.6401 (0.9259)  loss_classifier: 0.2742 (0.4539)  loss_box_reg: 0.2279 (0.2099)  loss_objectness: 0.0933 (0.1734)  loss_rpn_box_reg: 0.0638 (0.0888)  time: 0.2151  data: 0.0057  max mem: 7732\n",
      "Epoch: [0]  [6400/7330]  eta: 0:03:21  lr: 0.000010  loss: 0.6893 (0.9228)  loss_classifier: 0.2670 (0.4514)  loss_box_reg: 0.2345 (0.2104)  loss_objectness: 0.0972 (0.1723)  loss_rpn_box_reg: 0.0927 (0.0887)  time: 0.2190  data: 0.0057  max mem: 7732\n",
      "Epoch: [0]  [6500/7330]  eta: 0:02:59  lr: 0.000010  loss: 0.6811 (0.9193)  loss_classifier: 0.2874 (0.4489)  loss_box_reg: 0.2385 (0.2108)  loss_objectness: 0.0876 (0.1711)  loss_rpn_box_reg: 0.0686 (0.0886)  time: 0.2163  data: 0.0056  max mem: 7732\n",
      "Epoch: [0]  [6600/7330]  eta: 0:02:37  lr: 0.000010  loss: 0.7447 (0.9163)  loss_classifier: 0.3078 (0.4465)  loss_box_reg: 0.2491 (0.2112)  loss_objectness: 0.0971 (0.1701)  loss_rpn_box_reg: 0.0719 (0.0885)  time: 0.2225  data: 0.0058  max mem: 7732\n",
      "Epoch: [0]  [6700/7330]  eta: 0:02:16  lr: 0.000010  loss: 0.6410 (0.9134)  loss_classifier: 0.2716 (0.4442)  loss_box_reg: 0.2185 (0.2117)  loss_objectness: 0.0890 (0.1691)  loss_rpn_box_reg: 0.0705 (0.0885)  time: 0.2148  data: 0.0056  max mem: 7732\n",
      "Epoch: [0]  [6800/7330]  eta: 0:01:54  lr: 0.000010  loss: 0.6908 (0.9105)  loss_classifier: 0.2881 (0.4420)  loss_box_reg: 0.2224 (0.2120)  loss_objectness: 0.0913 (0.1681)  loss_rpn_box_reg: 0.0772 (0.0884)  time: 0.2164  data: 0.0060  max mem: 7732\n",
      "Epoch: [0]  [6900/7330]  eta: 0:01:32  lr: 0.000010  loss: 0.6846 (0.9074)  loss_classifier: 0.2674 (0.4396)  loss_box_reg: 0.2255 (0.2124)  loss_objectness: 0.0836 (0.1671)  loss_rpn_box_reg: 0.0703 (0.0883)  time: 0.2239  data: 0.0055  max mem: 7732\n",
      "Epoch: [0]  [7000/7330]  eta: 0:01:11  lr: 0.000010  loss: 0.7324 (0.9044)  loss_classifier: 0.2961 (0.4375)  loss_box_reg: 0.2387 (0.2127)  loss_objectness: 0.1062 (0.1661)  loss_rpn_box_reg: 0.0805 (0.0881)  time: 0.2146  data: 0.0055  max mem: 7732\n",
      "Epoch: [0]  [7100/7330]  eta: 0:00:49  lr: 0.000010  loss: 0.7223 (0.9017)  loss_classifier: 0.2857 (0.4354)  loss_box_reg: 0.2239 (0.2131)  loss_objectness: 0.0945 (0.1652)  loss_rpn_box_reg: 0.0736 (0.0881)  time: 0.2204  data: 0.0055  max mem: 7732\n",
      "Epoch: [0]  [7200/7330]  eta: 0:00:28  lr: 0.000010  loss: 0.6838 (0.8987)  loss_classifier: 0.2782 (0.4332)  loss_box_reg: 0.2448 (0.2133)  loss_objectness: 0.0952 (0.1643)  loss_rpn_box_reg: 0.0705 (0.0880)  time: 0.2214  data: 0.0061  max mem: 7732\n",
      "Epoch: [0]  [7300/7330]  eta: 0:00:06  lr: 0.000010  loss: 0.7502 (0.8962)  loss_classifier: 0.2955 (0.4312)  loss_box_reg: 0.2433 (0.2137)  loss_objectness: 0.0975 (0.1634)  loss_rpn_box_reg: 0.0952 (0.0879)  time: 0.2179  data: 0.0056  max mem: 7732\n",
      "Epoch: [0]  [7329/7330]  eta: 0:00:00  lr: 0.000010  loss: 0.6542 (0.8953)  loss_classifier: 0.2572 (0.4306)  loss_box_reg: 0.2143 (0.2137)  loss_objectness: 0.0912 (0.1632)  loss_rpn_box_reg: 0.0782 (0.0879)  time: 0.2042  data: 0.0055  max mem: 7732\n",
      "Epoch: [0] Total time: 0:26:25 (0.2164 s / it)\n",
      "Test:  [  0/313]  eta: 0:02:20  model_time: 0.1781 (0.1781)  evaluator_time: 0.0265 (0.0265)  time: 0.4493  data: 0.2361  max mem: 8218\n",
      "Test:  [312/313]  eta: 0:00:00  model_time: 0.1291 (0.1305)  evaluator_time: 0.0178 (0.0245)  time: 0.1569  data: 0.0058  max mem: 9052\n",
      "Test: Total time: 0:00:52 (0.1682 s / it)\n",
      "Averaged stats: model_time: 0.1291 (0.1305)  evaluator_time: 0.0178 (0.0245)\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.03s).\n",
      "IoU metric: bbox\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.250\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.537\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.204\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.063\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.319\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.377\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.209\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.244\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.252\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.068\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.319\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.377\n",
      "Epoch: [1]  [   0/7330]  eta: 1:40:58  lr: 0.000010  loss: 0.8666 (0.8666)  loss_classifier: 0.3463 (0.3463)  loss_box_reg: 0.2999 (0.2999)  loss_objectness: 0.1206 (0.1206)  loss_rpn_box_reg: 0.0998 (0.0998)  time: 0.8265  data: 0.6151  max mem: 9052\n",
      "Epoch: [1]  [ 100/7330]  eta: 0:27:12  lr: 0.000010  loss: 0.7123 (0.6935)  loss_classifier: 0.2845 (0.2827)  loss_box_reg: 0.2384 (0.2366)  loss_objectness: 0.0955 (0.0950)  loss_rpn_box_reg: 0.0773 (0.0792)  time: 0.2181  data: 0.0061  max mem: 9052\n",
      "Epoch: [1]  [ 200/7330]  eta: 0:26:27  lr: 0.000010  loss: 0.7094 (0.6876)  loss_classifier: 0.2824 (0.2801)  loss_box_reg: 0.2392 (0.2339)  loss_objectness: 0.0969 (0.0951)  loss_rpn_box_reg: 0.0761 (0.0784)  time: 0.2195  data: 0.0062  max mem: 9052\n",
      "Epoch: [1]  [ 300/7330]  eta: 0:25:49  lr: 0.000010  loss: 0.6924 (0.6920)  loss_classifier: 0.2868 (0.2818)  loss_box_reg: 0.2354 (0.2354)  loss_objectness: 0.0929 (0.0958)  loss_rpn_box_reg: 0.0727 (0.0791)  time: 0.2160  data: 0.0062  max mem: 9052\n",
      "Epoch: [1]  [ 400/7330]  eta: 0:25:25  lr: 0.000010  loss: 0.6769 (0.6911)  loss_classifier: 0.2780 (0.2826)  loss_box_reg: 0.2323 (0.2342)  loss_objectness: 0.0859 (0.0957)  loss_rpn_box_reg: 0.0690 (0.0786)  time: 0.2196  data: 0.0062  max mem: 9052\n",
      "Epoch: [1]  [ 500/7330]  eta: 0:25:01  lr: 0.000010  loss: 0.7543 (0.6958)  loss_classifier: 0.2987 (0.2843)  loss_box_reg: 0.2471 (0.2353)  loss_objectness: 0.1007 (0.0964)  loss_rpn_box_reg: 0.0898 (0.0798)  time: 0.2198  data: 0.0062  max mem: 9052\n",
      "Epoch: [1]  [ 600/7330]  eta: 0:24:38  lr: 0.000010  loss: 0.7540 (0.6957)  loss_classifier: 0.3045 (0.2838)  loss_box_reg: 0.2532 (0.2351)  loss_objectness: 0.0917 (0.0963)  loss_rpn_box_reg: 0.0967 (0.0806)  time: 0.2246  data: 0.0061  max mem: 9052\n",
      "Epoch: [1]  [ 700/7330]  eta: 0:24:17  lr: 0.000010  loss: 0.7024 (0.6954)  loss_classifier: 0.2751 (0.2834)  loss_box_reg: 0.2294 (0.2357)  loss_objectness: 0.0867 (0.0959)  loss_rpn_box_reg: 0.0759 (0.0804)  time: 0.2218  data: 0.0059  max mem: 9052\n",
      "Epoch: [1]  [ 800/7330]  eta: 0:23:56  lr: 0.000010  loss: 0.7275 (0.6936)  loss_classifier: 0.3050 (0.2827)  loss_box_reg: 0.2419 (0.2353)  loss_objectness: 0.0927 (0.0956)  loss_rpn_box_reg: 0.0761 (0.0800)  time: 0.2235  data: 0.0060  max mem: 9052\n",
      "Epoch: [1]  [ 900/7330]  eta: 0:23:33  lr: 0.000010  loss: 0.6639 (0.6934)  loss_classifier: 0.2709 (0.2826)  loss_box_reg: 0.2107 (0.2354)  loss_objectness: 0.0835 (0.0956)  loss_rpn_box_reg: 0.0740 (0.0798)  time: 0.2197  data: 0.0059  max mem: 9052\n",
      "Epoch: [1]  [1000/7330]  eta: 0:23:11  lr: 0.000010  loss: 0.6614 (0.6923)  loss_classifier: 0.2624 (0.2820)  loss_box_reg: 0.2306 (0.2352)  loss_objectness: 0.0928 (0.0954)  loss_rpn_box_reg: 0.0716 (0.0797)  time: 0.2171  data: 0.0060  max mem: 9052\n",
      "Epoch: [1]  [1100/7330]  eta: 0:22:49  lr: 0.000010  loss: 0.6640 (0.6919)  loss_classifier: 0.2528 (0.2817)  loss_box_reg: 0.2398 (0.2355)  loss_objectness: 0.0854 (0.0951)  loss_rpn_box_reg: 0.0707 (0.0796)  time: 0.2209  data: 0.0062  max mem: 9052\n",
      "Epoch: [1]  [1200/7330]  eta: 0:22:26  lr: 0.000010  loss: 0.6647 (0.6920)  loss_classifier: 0.2682 (0.2814)  loss_box_reg: 0.2252 (0.2356)  loss_objectness: 0.0902 (0.0955)  loss_rpn_box_reg: 0.0628 (0.0795)  time: 0.2178  data: 0.0060  max mem: 9052\n",
      "Epoch: [1]  [1300/7330]  eta: 0:22:03  lr: 0.000010  loss: 0.7150 (0.6917)  loss_classifier: 0.2821 (0.2814)  loss_box_reg: 0.2458 (0.2359)  loss_objectness: 0.0957 (0.0952)  loss_rpn_box_reg: 0.0745 (0.0792)  time: 0.2147  data: 0.0062  max mem: 9052\n",
      "Epoch: [1]  [1400/7330]  eta: 0:21:41  lr: 0.000010  loss: 0.6594 (0.6912)  loss_classifier: 0.2715 (0.2810)  loss_box_reg: 0.2314 (0.2360)  loss_objectness: 0.0933 (0.0950)  loss_rpn_box_reg: 0.0716 (0.0791)  time: 0.2202  data: 0.0060  max mem: 9052\n",
      "Epoch: [1]  [1500/7330]  eta: 0:21:19  lr: 0.000010  loss: 0.7117 (0.6924)  loss_classifier: 0.2898 (0.2813)  loss_box_reg: 0.2360 (0.2364)  loss_objectness: 0.0959 (0.0951)  loss_rpn_box_reg: 0.0734 (0.0796)  time: 0.2203  data: 0.0063  max mem: 9052\n",
      "Epoch: [1]  [1600/7330]  eta: 0:20:57  lr: 0.000010  loss: 0.6379 (0.6919)  loss_classifier: 0.2704 (0.2812)  loss_box_reg: 0.2240 (0.2363)  loss_objectness: 0.0891 (0.0950)  loss_rpn_box_reg: 0.0629 (0.0793)  time: 0.2178  data: 0.0062  max mem: 9052\n",
      "Epoch: [1]  [1700/7330]  eta: 0:20:35  lr: 0.000010  loss: 0.6500 (0.6917)  loss_classifier: 0.2600 (0.2811)  loss_box_reg: 0.2340 (0.2361)  loss_objectness: 0.0907 (0.0951)  loss_rpn_box_reg: 0.0687 (0.0793)  time: 0.2182  data: 0.0061  max mem: 9052\n",
      "Epoch: [1]  [1800/7330]  eta: 0:20:12  lr: 0.000010  loss: 0.7152 (0.6922)  loss_classifier: 0.2902 (0.2813)  loss_box_reg: 0.2355 (0.2363)  loss_objectness: 0.0934 (0.0952)  loss_rpn_box_reg: 0.0844 (0.0794)  time: 0.2185  data: 0.0059  max mem: 9052\n",
      "Epoch: [1]  [1900/7330]  eta: 0:19:50  lr: 0.000010  loss: 0.6565 (0.6917)  loss_classifier: 0.2684 (0.2813)  loss_box_reg: 0.2154 (0.2360)  loss_objectness: 0.0882 (0.0952)  loss_rpn_box_reg: 0.0754 (0.0792)  time: 0.2138  data: 0.0058  max mem: 9052\n",
      "Epoch: [1]  [2000/7330]  eta: 0:19:28  lr: 0.000010  loss: 0.6866 (0.6911)  loss_classifier: 0.2756 (0.2810)  loss_box_reg: 0.2334 (0.2360)  loss_objectness: 0.0873 (0.0950)  loss_rpn_box_reg: 0.0794 (0.0792)  time: 0.2197  data: 0.0060  max mem: 9052\n",
      "Epoch: [1]  [2100/7330]  eta: 0:19:06  lr: 0.000010  loss: 0.7156 (0.6909)  loss_classifier: 0.2812 (0.2809)  loss_box_reg: 0.2395 (0.2359)  loss_objectness: 0.0921 (0.0949)  loss_rpn_box_reg: 0.0743 (0.0792)  time: 0.2175  data: 0.0060  max mem: 9052\n",
      "Epoch: [1]  [2200/7330]  eta: 0:18:44  lr: 0.000010  loss: 0.6360 (0.6904)  loss_classifier: 0.2583 (0.2808)  loss_box_reg: 0.2142 (0.2358)  loss_objectness: 0.0868 (0.0948)  loss_rpn_box_reg: 0.0635 (0.0790)  time: 0.2162  data: 0.0059  max mem: 9052\n",
      "Epoch: [1]  [2300/7330]  eta: 0:18:22  lr: 0.000009  loss: 0.6709 (0.6903)  loss_classifier: 0.2663 (0.2808)  loss_box_reg: 0.2477 (0.2358)  loss_objectness: 0.0879 (0.0947)  loss_rpn_box_reg: 0.0748 (0.0790)  time: 0.2170  data: 0.0060  max mem: 9052\n",
      "Epoch: [1]  [2400/7330]  eta: 0:18:00  lr: 0.000009  loss: 0.6922 (0.6909)  loss_classifier: 0.2826 (0.2809)  loss_box_reg: 0.2382 (0.2360)  loss_objectness: 0.0821 (0.0947)  loss_rpn_box_reg: 0.0729 (0.0793)  time: 0.2251  data: 0.0059  max mem: 9052\n",
      "Epoch: [1]  [2500/7330]  eta: 0:17:38  lr: 0.000009  loss: 0.6463 (0.6893)  loss_classifier: 0.2575 (0.2803)  loss_box_reg: 0.2295 (0.2355)  loss_objectness: 0.0853 (0.0945)  loss_rpn_box_reg: 0.0707 (0.0791)  time: 0.2225  data: 0.0058  max mem: 9052\n",
      "Epoch: [1]  [2600/7330]  eta: 0:17:16  lr: 0.000009  loss: 0.6863 (0.6895)  loss_classifier: 0.2607 (0.2802)  loss_box_reg: 0.2320 (0.2358)  loss_objectness: 0.0890 (0.0944)  loss_rpn_box_reg: 0.0870 (0.0792)  time: 0.2170  data: 0.0059  max mem: 9052\n",
      "Epoch: [1]  [2700/7330]  eta: 0:16:54  lr: 0.000009  loss: 0.6616 (0.6895)  loss_classifier: 0.2683 (0.2802)  loss_box_reg: 0.2290 (0.2360)  loss_objectness: 0.0779 (0.0942)  loss_rpn_box_reg: 0.0718 (0.0792)  time: 0.2222  data: 0.0059  max mem: 9052\n",
      "Epoch: [1]  [2800/7330]  eta: 0:16:32  lr: 0.000009  loss: 0.6898 (0.6891)  loss_classifier: 0.2669 (0.2801)  loss_box_reg: 0.2337 (0.2360)  loss_objectness: 0.0865 (0.0941)  loss_rpn_box_reg: 0.0778 (0.0789)  time: 0.2168  data: 0.0059  max mem: 9052\n",
      "Epoch: [1]  [2900/7330]  eta: 0:16:11  lr: 0.000009  loss: 0.7212 (0.6889)  loss_classifier: 0.2804 (0.2799)  loss_box_reg: 0.2447 (0.2361)  loss_objectness: 0.0904 (0.0939)  loss_rpn_box_reg: 0.0838 (0.0789)  time: 0.2174  data: 0.0063  max mem: 9052\n",
      "Epoch: [1]  [3000/7330]  eta: 0:15:48  lr: 0.000009  loss: 0.6080 (0.6888)  loss_classifier: 0.2522 (0.2799)  loss_box_reg: 0.2125 (0.2362)  loss_objectness: 0.0862 (0.0938)  loss_rpn_box_reg: 0.0799 (0.0789)  time: 0.2148  data: 0.0058  max mem: 9052\n",
      "Epoch: [1]  [3100/7330]  eta: 0:15:26  lr: 0.000009  loss: 0.6918 (0.6887)  loss_classifier: 0.2762 (0.2798)  loss_box_reg: 0.2355 (0.2363)  loss_objectness: 0.0945 (0.0938)  loss_rpn_box_reg: 0.0766 (0.0788)  time: 0.2178  data: 0.0060  max mem: 9052\n",
      "Epoch: [1]  [3200/7330]  eta: 0:15:04  lr: 0.000009  loss: 0.6708 (0.6892)  loss_classifier: 0.2717 (0.2800)  loss_box_reg: 0.2468 (0.2367)  loss_objectness: 0.0829 (0.0937)  loss_rpn_box_reg: 0.0647 (0.0788)  time: 0.2189  data: 0.0063  max mem: 9052\n",
      "Epoch: [1]  [3300/7330]  eta: 0:14:42  lr: 0.000009  loss: 0.7145 (0.6898)  loss_classifier: 0.2839 (0.2802)  loss_box_reg: 0.2361 (0.2369)  loss_objectness: 0.0845 (0.0937)  loss_rpn_box_reg: 0.0889 (0.0790)  time: 0.2168  data: 0.0060  max mem: 9052\n",
      "Epoch: [1]  [3400/7330]  eta: 0:14:21  lr: 0.000009  loss: 0.6756 (0.6896)  loss_classifier: 0.2748 (0.2801)  loss_box_reg: 0.2470 (0.2370)  loss_objectness: 0.0898 (0.0936)  loss_rpn_box_reg: 0.0750 (0.0790)  time: 0.2166  data: 0.0059  max mem: 9052\n",
      "Epoch: [1]  [3500/7330]  eta: 0:13:59  lr: 0.000009  loss: 0.6824 (0.6894)  loss_classifier: 0.2678 (0.2800)  loss_box_reg: 0.2368 (0.2370)  loss_objectness: 0.0820 (0.0935)  loss_rpn_box_reg: 0.0751 (0.0790)  time: 0.2206  data: 0.0059  max mem: 9052\n",
      "Epoch: [1]  [3600/7330]  eta: 0:13:37  lr: 0.000009  loss: 0.7176 (0.6893)  loss_classifier: 0.2989 (0.2800)  loss_box_reg: 0.2481 (0.2371)  loss_objectness: 0.0934 (0.0934)  loss_rpn_box_reg: 0.0805 (0.0788)  time: 0.2212  data: 0.0059  max mem: 9052\n",
      "Epoch: [1]  [3700/7330]  eta: 0:13:15  lr: 0.000009  loss: 0.6566 (0.6891)  loss_classifier: 0.2704 (0.2799)  loss_box_reg: 0.2253 (0.2371)  loss_objectness: 0.0842 (0.0933)  loss_rpn_box_reg: 0.0724 (0.0788)  time: 0.2232  data: 0.0059  max mem: 9052\n",
      "Epoch: [1]  [3800/7330]  eta: 0:12:53  lr: 0.000009  loss: 0.7090 (0.6886)  loss_classifier: 0.2844 (0.2797)  loss_box_reg: 0.2441 (0.2371)  loss_objectness: 0.0927 (0.0932)  loss_rpn_box_reg: 0.0746 (0.0787)  time: 0.2093  data: 0.0057  max mem: 9052\n",
      "Epoch: [1]  [3900/7330]  eta: 0:12:31  lr: 0.000009  loss: 0.7023 (0.6884)  loss_classifier: 0.2781 (0.2796)  loss_box_reg: 0.2540 (0.2371)  loss_objectness: 0.0838 (0.0931)  loss_rpn_box_reg: 0.0756 (0.0785)  time: 0.2193  data: 0.0059  max mem: 9052\n",
      "Epoch: [1]  [4000/7330]  eta: 0:12:09  lr: 0.000009  loss: 0.7200 (0.6884)  loss_classifier: 0.3008 (0.2796)  loss_box_reg: 0.2508 (0.2372)  loss_objectness: 0.0905 (0.0930)  loss_rpn_box_reg: 0.0772 (0.0785)  time: 0.2168  data: 0.0058  max mem: 9052\n",
      "Epoch: [1]  [4100/7330]  eta: 0:11:47  lr: 0.000009  loss: 0.6840 (0.6883)  loss_classifier: 0.2754 (0.2796)  loss_box_reg: 0.2471 (0.2373)  loss_objectness: 0.0884 (0.0929)  loss_rpn_box_reg: 0.0692 (0.0785)  time: 0.2186  data: 0.0058  max mem: 9052\n",
      "Epoch: [1]  [4200/7330]  eta: 0:11:25  lr: 0.000009  loss: 0.6343 (0.6880)  loss_classifier: 0.2589 (0.2794)  loss_box_reg: 0.2245 (0.2373)  loss_objectness: 0.0831 (0.0929)  loss_rpn_box_reg: 0.0603 (0.0785)  time: 0.2153  data: 0.0058  max mem: 9052\n",
      "Epoch: [1]  [4300/7330]  eta: 0:11:03  lr: 0.000009  loss: 0.6949 (0.6880)  loss_classifier: 0.2819 (0.2793)  loss_box_reg: 0.2427 (0.2373)  loss_objectness: 0.0893 (0.0928)  loss_rpn_box_reg: 0.0669 (0.0786)  time: 0.2164  data: 0.0062  max mem: 9052\n",
      "Epoch: [1]  [4400/7330]  eta: 0:10:41  lr: 0.000009  loss: 0.6877 (0.6879)  loss_classifier: 0.2817 (0.2792)  loss_box_reg: 0.2433 (0.2375)  loss_objectness: 0.0838 (0.0927)  loss_rpn_box_reg: 0.0771 (0.0785)  time: 0.2205  data: 0.0060  max mem: 9052\n",
      "Epoch: [1]  [4500/7330]  eta: 0:10:19  lr: 0.000009  loss: 0.6881 (0.6877)  loss_classifier: 0.2799 (0.2791)  loss_box_reg: 0.2547 (0.2375)  loss_objectness: 0.0808 (0.0926)  loss_rpn_box_reg: 0.0704 (0.0785)  time: 0.2195  data: 0.0060  max mem: 9052\n",
      "Epoch: [1]  [4600/7330]  eta: 0:09:57  lr: 0.000009  loss: 0.6899 (0.6876)  loss_classifier: 0.2959 (0.2791)  loss_box_reg: 0.2475 (0.2376)  loss_objectness: 0.0906 (0.0926)  loss_rpn_box_reg: 0.0764 (0.0784)  time: 0.2247  data: 0.0061  max mem: 9052\n",
      "Epoch: [1]  [4700/7330]  eta: 0:09:35  lr: 0.000009  loss: 0.6581 (0.6877)  loss_classifier: 0.2674 (0.2791)  loss_box_reg: 0.2450 (0.2377)  loss_objectness: 0.0854 (0.0925)  loss_rpn_box_reg: 0.0745 (0.0783)  time: 0.2191  data: 0.0059  max mem: 9052\n",
      "Epoch: [1]  [4800/7330]  eta: 0:09:13  lr: 0.000009  loss: 0.6408 (0.6872)  loss_classifier: 0.2607 (0.2789)  loss_box_reg: 0.2251 (0.2376)  loss_objectness: 0.0832 (0.0924)  loss_rpn_box_reg: 0.0753 (0.0783)  time: 0.2189  data: 0.0059  max mem: 9052\n",
      "Epoch: [1]  [4900/7330]  eta: 0:08:52  lr: 0.000009  loss: 0.6165 (0.6867)  loss_classifier: 0.2482 (0.2787)  loss_box_reg: 0.2020 (0.2375)  loss_objectness: 0.0860 (0.0923)  loss_rpn_box_reg: 0.0717 (0.0782)  time: 0.2236  data: 0.0063  max mem: 9052\n",
      "Epoch: [1]  [5000/7330]  eta: 0:08:30  lr: 0.000009  loss: 0.6999 (0.6866)  loss_classifier: 0.2851 (0.2787)  loss_box_reg: 0.2565 (0.2376)  loss_objectness: 0.0899 (0.0923)  loss_rpn_box_reg: 0.0691 (0.0781)  time: 0.2158  data: 0.0059  max mem: 9052\n",
      "Epoch: [1]  [5100/7330]  eta: 0:08:08  lr: 0.000009  loss: 0.6228 (0.6860)  loss_classifier: 0.2475 (0.2784)  loss_box_reg: 0.2122 (0.2375)  loss_objectness: 0.0770 (0.0921)  loss_rpn_box_reg: 0.0788 (0.0780)  time: 0.2124  data: 0.0060  max mem: 9052\n",
      "Epoch: [1]  [5200/7330]  eta: 0:07:46  lr: 0.000009  loss: 0.7419 (0.6860)  loss_classifier: 0.3011 (0.2784)  loss_box_reg: 0.2524 (0.2375)  loss_objectness: 0.0936 (0.0921)  loss_rpn_box_reg: 0.0828 (0.0780)  time: 0.2193  data: 0.0059  max mem: 9052\n",
      "Epoch: [1]  [5300/7330]  eta: 0:07:24  lr: 0.000009  loss: 0.6312 (0.6861)  loss_classifier: 0.2717 (0.2784)  loss_box_reg: 0.2222 (0.2376)  loss_objectness: 0.0821 (0.0921)  loss_rpn_box_reg: 0.0747 (0.0780)  time: 0.2138  data: 0.0060  max mem: 9052\n",
      "Epoch: [1]  [5400/7330]  eta: 0:07:02  lr: 0.000009  loss: 0.6688 (0.6859)  loss_classifier: 0.2832 (0.2783)  loss_box_reg: 0.2361 (0.2376)  loss_objectness: 0.0895 (0.0920)  loss_rpn_box_reg: 0.0594 (0.0780)  time: 0.2206  data: 0.0060  max mem: 9052\n",
      "Epoch: [1]  [5500/7330]  eta: 0:06:40  lr: 0.000009  loss: 0.6583 (0.6858)  loss_classifier: 0.2653 (0.2782)  loss_box_reg: 0.2297 (0.2376)  loss_objectness: 0.0798 (0.0919)  loss_rpn_box_reg: 0.0681 (0.0781)  time: 0.2140  data: 0.0060  max mem: 9052\n",
      "Epoch: [1]  [5600/7330]  eta: 0:06:18  lr: 0.000009  loss: 0.6450 (0.6859)  loss_classifier: 0.2697 (0.2783)  loss_box_reg: 0.2382 (0.2378)  loss_objectness: 0.0808 (0.0919)  loss_rpn_box_reg: 0.0611 (0.0780)  time: 0.2131  data: 0.0058  max mem: 9052\n",
      "Epoch: [1]  [5700/7330]  eta: 0:05:56  lr: 0.000009  loss: 0.6906 (0.6860)  loss_classifier: 0.2652 (0.2783)  loss_box_reg: 0.2469 (0.2379)  loss_objectness: 0.0851 (0.0918)  loss_rpn_box_reg: 0.0743 (0.0780)  time: 0.2192  data: 0.0062  max mem: 9052\n",
      "Epoch: [1]  [5800/7330]  eta: 0:05:34  lr: 0.000009  loss: 0.6429 (0.6857)  loss_classifier: 0.2633 (0.2782)  loss_box_reg: 0.2167 (0.2378)  loss_objectness: 0.0893 (0.0917)  loss_rpn_box_reg: 0.0710 (0.0780)  time: 0.2170  data: 0.0066  max mem: 9052\n",
      "Epoch: [1]  [5900/7330]  eta: 0:05:13  lr: 0.000009  loss: 0.6890 (0.6853)  loss_classifier: 0.2765 (0.2780)  loss_box_reg: 0.2245 (0.2377)  loss_objectness: 0.0895 (0.0916)  loss_rpn_box_reg: 0.0692 (0.0780)  time: 0.2188  data: 0.0061  max mem: 9052\n",
      "Epoch: [1]  [6000/7330]  eta: 0:04:51  lr: 0.000009  loss: 0.6876 (0.6853)  loss_classifier: 0.2804 (0.2780)  loss_box_reg: 0.2348 (0.2377)  loss_objectness: 0.0841 (0.0916)  loss_rpn_box_reg: 0.0793 (0.0780)  time: 0.2191  data: 0.0058  max mem: 9052\n",
      "Epoch: [1]  [6100/7330]  eta: 0:04:29  lr: 0.000009  loss: 0.6793 (0.6853)  loss_classifier: 0.2699 (0.2779)  loss_box_reg: 0.2367 (0.2378)  loss_objectness: 0.0796 (0.0915)  loss_rpn_box_reg: 0.0762 (0.0780)  time: 0.2153  data: 0.0059  max mem: 9052\n",
      "Epoch: [1]  [6200/7330]  eta: 0:04:07  lr: 0.000009  loss: 0.6019 (0.6849)  loss_classifier: 0.2496 (0.2778)  loss_box_reg: 0.2166 (0.2378)  loss_objectness: 0.0718 (0.0914)  loss_rpn_box_reg: 0.0663 (0.0779)  time: 0.2192  data: 0.0058  max mem: 9052\n",
      "Epoch: [1]  [6300/7330]  eta: 0:03:45  lr: 0.000009  loss: 0.6510 (0.6848)  loss_classifier: 0.2593 (0.2777)  loss_box_reg: 0.2237 (0.2378)  loss_objectness: 0.0801 (0.0913)  loss_rpn_box_reg: 0.0684 (0.0779)  time: 0.2236  data: 0.0065  max mem: 9052\n",
      "Epoch: [1]  [6400/7330]  eta: 0:03:23  lr: 0.000009  loss: 0.7047 (0.6846)  loss_classifier: 0.2781 (0.2776)  loss_box_reg: 0.2499 (0.2378)  loss_objectness: 0.0935 (0.0913)  loss_rpn_box_reg: 0.0788 (0.0779)  time: 0.2168  data: 0.0062  max mem: 9052\n",
      "Epoch: [1]  [6500/7330]  eta: 0:03:01  lr: 0.000009  loss: 0.6438 (0.6845)  loss_classifier: 0.2514 (0.2776)  loss_box_reg: 0.2175 (0.2378)  loss_objectness: 0.0811 (0.0912)  loss_rpn_box_reg: 0.0771 (0.0779)  time: 0.2235  data: 0.0062  max mem: 9052\n",
      "Epoch: [1]  [6600/7330]  eta: 0:02:39  lr: 0.000009  loss: 0.6286 (0.6843)  loss_classifier: 0.2522 (0.2774)  loss_box_reg: 0.2189 (0.2378)  loss_objectness: 0.0761 (0.0912)  loss_rpn_box_reg: 0.0782 (0.0779)  time: 0.2243  data: 0.0059  max mem: 9052\n",
      "Epoch: [1]  [6700/7330]  eta: 0:02:17  lr: 0.000009  loss: 0.7131 (0.6841)  loss_classifier: 0.2726 (0.2773)  loss_box_reg: 0.2573 (0.2378)  loss_objectness: 0.0877 (0.0911)  loss_rpn_box_reg: 0.0797 (0.0778)  time: 0.2102  data: 0.0060  max mem: 9052\n",
      "Epoch: [1]  [6800/7330]  eta: 0:01:56  lr: 0.000009  loss: 0.6851 (0.6838)  loss_classifier: 0.2715 (0.2772)  loss_box_reg: 0.2345 (0.2377)  loss_objectness: 0.0902 (0.0911)  loss_rpn_box_reg: 0.0689 (0.0778)  time: 0.2170  data: 0.0064  max mem: 9052\n",
      "Epoch: [1]  [6900/7330]  eta: 0:01:34  lr: 0.000009  loss: 0.6627 (0.6839)  loss_classifier: 0.2780 (0.2772)  loss_box_reg: 0.2379 (0.2378)  loss_objectness: 0.0824 (0.0910)  loss_rpn_box_reg: 0.0716 (0.0778)  time: 0.2125  data: 0.0060  max mem: 9052\n",
      "Epoch: [1]  [7000/7330]  eta: 0:01:12  lr: 0.000009  loss: 0.7295 (0.6838)  loss_classifier: 0.2914 (0.2772)  loss_box_reg: 0.2633 (0.2378)  loss_objectness: 0.0903 (0.0910)  loss_rpn_box_reg: 0.0833 (0.0778)  time: 0.2208  data: 0.0059  max mem: 9052\n",
      "Epoch: [1]  [7100/7330]  eta: 0:00:50  lr: 0.000009  loss: 0.7189 (0.6839)  loss_classifier: 0.2772 (0.2772)  loss_box_reg: 0.2382 (0.2379)  loss_objectness: 0.0867 (0.0910)  loss_rpn_box_reg: 0.0816 (0.0778)  time: 0.2154  data: 0.0061  max mem: 9052\n",
      "Epoch: [1]  [7200/7330]  eta: 0:00:28  lr: 0.000009  loss: 0.6456 (0.6836)  loss_classifier: 0.2663 (0.2771)  loss_box_reg: 0.2185 (0.2379)  loss_objectness: 0.0788 (0.0909)  loss_rpn_box_reg: 0.0782 (0.0778)  time: 0.2179  data: 0.0059  max mem: 9052\n",
      "Epoch: [1]  [7300/7330]  eta: 0:00:06  lr: 0.000009  loss: 0.6802 (0.6836)  loss_classifier: 0.2807 (0.2770)  loss_box_reg: 0.2507 (0.2379)  loss_objectness: 0.0875 (0.0908)  loss_rpn_box_reg: 0.0662 (0.0778)  time: 0.2220  data: 0.0060  max mem: 9052\n",
      "Epoch: [1]  [7329/7330]  eta: 0:00:00  lr: 0.000009  loss: 0.7011 (0.6836)  loss_classifier: 0.2765 (0.2770)  loss_box_reg: 0.2338 (0.2379)  loss_objectness: 0.0889 (0.0908)  loss_rpn_box_reg: 0.0734 (0.0778)  time: 0.2130  data: 0.0059  max mem: 9052\n",
      "Epoch: [1] Total time: 0:26:44 (0.2189 s / it)\n",
      "Test:  [  0/313]  eta: 0:02:19  model_time: 0.1379 (0.1379)  evaluator_time: 0.0287 (0.0287)  time: 0.4447  data: 0.2696  max mem: 9052\n",
      "Test:  [312/313]  eta: 0:00:00  model_time: 0.1288 (0.1299)  evaluator_time: 0.0186 (0.0248)  time: 0.1578  data: 0.0060  max mem: 9052\n",
      "Test: Total time: 0:00:52 (0.1684 s / it)\n",
      "Averaged stats: model_time: 0.1288 (0.1299)  evaluator_time: 0.0186 (0.0248)\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      "IoU metric: bbox\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.247\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.494\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.274\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.032\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.356\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.421\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.181\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.253\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.263\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.046\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.369\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.430\n",
      "Epoch: [2]  [   0/7330]  eta: 1:33:25  lr: 0.000009  loss: 0.7033 (0.7033)  loss_classifier: 0.2476 (0.2476)  loss_box_reg: 0.2231 (0.2231)  loss_objectness: 0.1034 (0.1034)  loss_rpn_box_reg: 0.1292 (0.1292)  time: 0.7648  data: 0.5425  max mem: 9052\n",
      "Epoch: [2]  [ 100/7330]  eta: 0:27:05  lr: 0.000009  loss: 0.5806 (0.6513)  loss_classifier: 0.2324 (0.2627)  loss_box_reg: 0.2137 (0.2333)  loss_objectness: 0.0787 (0.0821)  loss_rpn_box_reg: 0.0577 (0.0731)  time: 0.2205  data: 0.0064  max mem: 9052\n",
      "Epoch: [2]  [ 200/7330]  eta: 0:26:17  lr: 0.000009  loss: 0.6442 (0.6727)  loss_classifier: 0.2619 (0.2702)  loss_box_reg: 0.2185 (0.2384)  loss_objectness: 0.0857 (0.0868)  loss_rpn_box_reg: 0.0819 (0.0773)  time: 0.2197  data: 0.0064  max mem: 9052\n",
      "Epoch: [2]  [ 300/7330]  eta: 0:25:48  lr: 0.000009  loss: 0.6785 (0.6747)  loss_classifier: 0.2843 (0.2720)  loss_box_reg: 0.2292 (0.2378)  loss_objectness: 0.0828 (0.0870)  loss_rpn_box_reg: 0.0739 (0.0779)  time: 0.2176  data: 0.0062  max mem: 9052\n",
      "Epoch: [2]  [ 400/7330]  eta: 0:25:27  lr: 0.000009  loss: 0.6486 (0.6694)  loss_classifier: 0.2685 (0.2700)  loss_box_reg: 0.2185 (0.2358)  loss_objectness: 0.0896 (0.0864)  loss_rpn_box_reg: 0.0713 (0.0772)  time: 0.2142  data: 0.0067  max mem: 9052\n",
      "Epoch: [2]  [ 500/7330]  eta: 0:25:02  lr: 0.000009  loss: 0.6561 (0.6706)  loss_classifier: 0.2664 (0.2705)  loss_box_reg: 0.2279 (0.2360)  loss_objectness: 0.0758 (0.0863)  loss_rpn_box_reg: 0.0692 (0.0777)  time: 0.2174  data: 0.0064  max mem: 9052\n",
      "Epoch: [2]  [ 600/7330]  eta: 0:24:36  lr: 0.000009  loss: 0.6570 (0.6696)  loss_classifier: 0.2606 (0.2702)  loss_box_reg: 0.2330 (0.2357)  loss_objectness: 0.0811 (0.0859)  loss_rpn_box_reg: 0.0865 (0.0778)  time: 0.2175  data: 0.0063  max mem: 9052\n",
      "Epoch: [2]  [ 700/7330]  eta: 0:24:16  lr: 0.000009  loss: 0.6081 (0.6680)  loss_classifier: 0.2513 (0.2692)  loss_box_reg: 0.2105 (0.2353)  loss_objectness: 0.0707 (0.0858)  loss_rpn_box_reg: 0.0701 (0.0777)  time: 0.2204  data: 0.0064  max mem: 9052\n",
      "Epoch: [2]  [ 800/7330]  eta: 0:23:56  lr: 0.000008  loss: 0.5702 (0.6650)  loss_classifier: 0.2432 (0.2682)  loss_box_reg: 0.2106 (0.2348)  loss_objectness: 0.0765 (0.0853)  loss_rpn_box_reg: 0.0568 (0.0766)  time: 0.2233  data: 0.0063  max mem: 9052\n",
      "Epoch: [2]  [ 900/7330]  eta: 0:23:32  lr: 0.000008  loss: 0.6656 (0.6644)  loss_classifier: 0.2576 (0.2681)  loss_box_reg: 0.2351 (0.2348)  loss_objectness: 0.0862 (0.0853)  loss_rpn_box_reg: 0.0741 (0.0763)  time: 0.2218  data: 0.0063  max mem: 9052\n",
      "Epoch: [2]  [1000/7330]  eta: 0:23:10  lr: 0.000008  loss: 0.5734 (0.6636)  loss_classifier: 0.2416 (0.2678)  loss_box_reg: 0.2145 (0.2347)  loss_objectness: 0.0724 (0.0850)  loss_rpn_box_reg: 0.0635 (0.0761)  time: 0.2206  data: 0.0063  max mem: 9052\n",
      "Epoch: [2]  [1100/7330]  eta: 0:22:48  lr: 0.000008  loss: 0.7092 (0.6642)  loss_classifier: 0.2845 (0.2680)  loss_box_reg: 0.2462 (0.2350)  loss_objectness: 0.0826 (0.0850)  loss_rpn_box_reg: 0.0779 (0.0763)  time: 0.2173  data: 0.0063  max mem: 9052\n",
      "Epoch: [2]  [1200/7330]  eta: 0:22:25  lr: 0.000008  loss: 0.6120 (0.6638)  loss_classifier: 0.2458 (0.2677)  loss_box_reg: 0.2131 (0.2351)  loss_objectness: 0.0740 (0.0849)  loss_rpn_box_reg: 0.0645 (0.0760)  time: 0.2127  data: 0.0063  max mem: 9052\n",
      "Epoch: [2]  [1300/7330]  eta: 0:22:03  lr: 0.000008  loss: 0.6873 (0.6639)  loss_classifier: 0.2793 (0.2679)  loss_box_reg: 0.2484 (0.2352)  loss_objectness: 0.0849 (0.0850)  loss_rpn_box_reg: 0.0677 (0.0759)  time: 0.2142  data: 0.0061  max mem: 9052\n",
      "Epoch: [2]  [1400/7330]  eta: 0:21:40  lr: 0.000008  loss: 0.6403 (0.6625)  loss_classifier: 0.2488 (0.2672)  loss_box_reg: 0.2133 (0.2346)  loss_objectness: 0.0827 (0.0849)  loss_rpn_box_reg: 0.0848 (0.0758)  time: 0.2105  data: 0.0063  max mem: 9052\n",
      "Epoch: [2]  [1500/7330]  eta: 0:21:17  lr: 0.000008  loss: 0.6537 (0.6623)  loss_classifier: 0.2625 (0.2673)  loss_box_reg: 0.2266 (0.2344)  loss_objectness: 0.0836 (0.0849)  loss_rpn_box_reg: 0.0737 (0.0757)  time: 0.2156  data: 0.0063  max mem: 9052\n",
      "Epoch: [2]  [1600/7330]  eta: 0:20:55  lr: 0.000008  loss: 0.6448 (0.6621)  loss_classifier: 0.2558 (0.2670)  loss_box_reg: 0.2190 (0.2340)  loss_objectness: 0.0844 (0.0851)  loss_rpn_box_reg: 0.0737 (0.0759)  time: 0.2132  data: 0.0058  max mem: 9052\n",
      "Epoch: [2]  [1700/7330]  eta: 0:20:33  lr: 0.000008  loss: 0.6546 (0.6615)  loss_classifier: 0.2581 (0.2667)  loss_box_reg: 0.2065 (0.2339)  loss_objectness: 0.0852 (0.0850)  loss_rpn_box_reg: 0.0831 (0.0758)  time: 0.2176  data: 0.0064  max mem: 9052\n",
      "Epoch: [2]  [1800/7330]  eta: 0:20:11  lr: 0.000008  loss: 0.6845 (0.6611)  loss_classifier: 0.2861 (0.2666)  loss_box_reg: 0.2510 (0.2338)  loss_objectness: 0.0762 (0.0850)  loss_rpn_box_reg: 0.0787 (0.0757)  time: 0.2201  data: 0.0065  max mem: 9052\n",
      "Epoch: [2]  [1900/7330]  eta: 0:19:49  lr: 0.000008  loss: 0.6352 (0.6611)  loss_classifier: 0.2594 (0.2666)  loss_box_reg: 0.2341 (0.2339)  loss_objectness: 0.0877 (0.0849)  loss_rpn_box_reg: 0.0650 (0.0757)  time: 0.2154  data: 0.0060  max mem: 9052\n",
      "Epoch: [2]  [2000/7330]  eta: 0:19:28  lr: 0.000008  loss: 0.5937 (0.6606)  loss_classifier: 0.2423 (0.2663)  loss_box_reg: 0.2199 (0.2339)  loss_objectness: 0.0781 (0.0848)  loss_rpn_box_reg: 0.0569 (0.0756)  time: 0.2210  data: 0.0060  max mem: 9052\n",
      "Epoch: [2]  [2100/7330]  eta: 0:19:06  lr: 0.000008  loss: 0.6403 (0.6603)  loss_classifier: 0.2561 (0.2661)  loss_box_reg: 0.2209 (0.2338)  loss_objectness: 0.0751 (0.0848)  loss_rpn_box_reg: 0.0678 (0.0756)  time: 0.2190  data: 0.0061  max mem: 9052\n",
      "Epoch: [2]  [2200/7330]  eta: 0:18:44  lr: 0.000008  loss: 0.5888 (0.6600)  loss_classifier: 0.2409 (0.2662)  loss_box_reg: 0.2103 (0.2338)  loss_objectness: 0.0778 (0.0846)  loss_rpn_box_reg: 0.0624 (0.0754)  time: 0.2151  data: 0.0062  max mem: 9052\n",
      "Epoch: [2]  [2300/7330]  eta: 0:18:22  lr: 0.000008  loss: 0.6491 (0.6596)  loss_classifier: 0.2579 (0.2660)  loss_box_reg: 0.2455 (0.2338)  loss_objectness: 0.0784 (0.0846)  loss_rpn_box_reg: 0.0669 (0.0753)  time: 0.2211  data: 0.0060  max mem: 9052\n",
      "Epoch: [2]  [2400/7330]  eta: 0:18:00  lr: 0.000008  loss: 0.6361 (0.6595)  loss_classifier: 0.2522 (0.2659)  loss_box_reg: 0.2258 (0.2337)  loss_objectness: 0.0755 (0.0845)  loss_rpn_box_reg: 0.0731 (0.0753)  time: 0.2194  data: 0.0064  max mem: 9052\n",
      "Epoch: [2]  [2500/7330]  eta: 0:17:38  lr: 0.000008  loss: 0.6524 (0.6604)  loss_classifier: 0.2698 (0.2663)  loss_box_reg: 0.2255 (0.2340)  loss_objectness: 0.0882 (0.0846)  loss_rpn_box_reg: 0.0793 (0.0755)  time: 0.2141  data: 0.0059  max mem: 9052\n",
      "Epoch: [2]  [2600/7330]  eta: 0:17:17  lr: 0.000008  loss: 0.6283 (0.6601)  loss_classifier: 0.2580 (0.2662)  loss_box_reg: 0.2286 (0.2340)  loss_objectness: 0.0846 (0.0845)  loss_rpn_box_reg: 0.0598 (0.0754)  time: 0.2201  data: 0.0062  max mem: 9052\n",
      "Epoch: [2]  [2700/7330]  eta: 0:16:55  lr: 0.000008  loss: 0.6068 (0.6601)  loss_classifier: 0.2560 (0.2662)  loss_box_reg: 0.2164 (0.2342)  loss_objectness: 0.0784 (0.0844)  loss_rpn_box_reg: 0.0600 (0.0753)  time: 0.2168  data: 0.0062  max mem: 9052\n",
      "Epoch: [2]  [2800/7330]  eta: 0:16:33  lr: 0.000008  loss: 0.6733 (0.6610)  loss_classifier: 0.2747 (0.2666)  loss_box_reg: 0.2296 (0.2346)  loss_objectness: 0.0821 (0.0844)  loss_rpn_box_reg: 0.0755 (0.0754)  time: 0.2181  data: 0.0061  max mem: 9052\n",
      "Epoch: [2]  [2900/7330]  eta: 0:16:11  lr: 0.000008  loss: 0.6352 (0.6611)  loss_classifier: 0.2607 (0.2666)  loss_box_reg: 0.2286 (0.2346)  loss_objectness: 0.0815 (0.0844)  loss_rpn_box_reg: 0.0711 (0.0755)  time: 0.2221  data: 0.0062  max mem: 9052\n",
      "Epoch: [2]  [3000/7330]  eta: 0:15:49  lr: 0.000008  loss: 0.6043 (0.6607)  loss_classifier: 0.2482 (0.2665)  loss_box_reg: 0.2184 (0.2346)  loss_objectness: 0.0716 (0.0843)  loss_rpn_box_reg: 0.0695 (0.0754)  time: 0.2207  data: 0.0061  max mem: 9052\n",
      "Epoch: [2]  [3100/7330]  eta: 0:15:28  lr: 0.000008  loss: 0.6442 (0.6608)  loss_classifier: 0.2508 (0.2665)  loss_box_reg: 0.2313 (0.2346)  loss_objectness: 0.0793 (0.0844)  loss_rpn_box_reg: 0.0655 (0.0753)  time: 0.2185  data: 0.0060  max mem: 9052\n",
      "Epoch: [2]  [3200/7330]  eta: 0:15:06  lr: 0.000008  loss: 0.6460 (0.6600)  loss_classifier: 0.2581 (0.2662)  loss_box_reg: 0.2182 (0.2343)  loss_objectness: 0.0877 (0.0843)  loss_rpn_box_reg: 0.0667 (0.0752)  time: 0.2151  data: 0.0062  max mem: 9052\n",
      "Epoch: [2]  [3300/7330]  eta: 0:14:44  lr: 0.000008  loss: 0.6469 (0.6598)  loss_classifier: 0.2499 (0.2661)  loss_box_reg: 0.2158 (0.2342)  loss_objectness: 0.0783 (0.0842)  loss_rpn_box_reg: 0.0822 (0.0752)  time: 0.2224  data: 0.0063  max mem: 9052\n",
      "Epoch: [2]  [3400/7330]  eta: 0:14:22  lr: 0.000008  loss: 0.6065 (0.6594)  loss_classifier: 0.2466 (0.2659)  loss_box_reg: 0.2150 (0.2341)  loss_objectness: 0.0756 (0.0842)  loss_rpn_box_reg: 0.0616 (0.0752)  time: 0.2233  data: 0.0059  max mem: 9052\n",
      "Epoch: [2]  [3500/7330]  eta: 0:14:00  lr: 0.000008  loss: 0.6938 (0.6598)  loss_classifier: 0.2800 (0.2661)  loss_box_reg: 0.2382 (0.2342)  loss_objectness: 0.0863 (0.0842)  loss_rpn_box_reg: 0.0713 (0.0752)  time: 0.2177  data: 0.0059  max mem: 9052\n",
      "Epoch: [2]  [3600/7330]  eta: 0:13:38  lr: 0.000008  loss: 0.6381 (0.6594)  loss_classifier: 0.2591 (0.2659)  loss_box_reg: 0.2314 (0.2341)  loss_objectness: 0.0864 (0.0842)  loss_rpn_box_reg: 0.0709 (0.0751)  time: 0.2203  data: 0.0060  max mem: 9052\n",
      "Epoch: [2]  [3700/7330]  eta: 0:13:16  lr: 0.000008  loss: 0.6971 (0.6599)  loss_classifier: 0.2618 (0.2661)  loss_box_reg: 0.2379 (0.2343)  loss_objectness: 0.0879 (0.0842)  loss_rpn_box_reg: 0.0767 (0.0752)  time: 0.2140  data: 0.0060  max mem: 9052\n",
      "Epoch: [2]  [3800/7330]  eta: 0:12:54  lr: 0.000008  loss: 0.6218 (0.6598)  loss_classifier: 0.2593 (0.2661)  loss_box_reg: 0.2247 (0.2344)  loss_objectness: 0.0775 (0.0842)  loss_rpn_box_reg: 0.0684 (0.0751)  time: 0.2210  data: 0.0060  max mem: 9052\n",
      "Epoch: [2]  [3900/7330]  eta: 0:12:32  lr: 0.000008  loss: 0.6510 (0.6598)  loss_classifier: 0.2640 (0.2661)  loss_box_reg: 0.2325 (0.2344)  loss_objectness: 0.0776 (0.0842)  loss_rpn_box_reg: 0.0661 (0.0751)  time: 0.2157  data: 0.0063  max mem: 9052\n",
      "Epoch: [2]  [4000/7330]  eta: 0:12:10  lr: 0.000008  loss: 0.6236 (0.6599)  loss_classifier: 0.2542 (0.2662)  loss_box_reg: 0.2208 (0.2345)  loss_objectness: 0.0787 (0.0842)  loss_rpn_box_reg: 0.0664 (0.0750)  time: 0.2182  data: 0.0064  max mem: 9052\n",
      "Epoch: [2]  [4100/7330]  eta: 0:11:48  lr: 0.000008  loss: 0.6894 (0.6600)  loss_classifier: 0.2611 (0.2662)  loss_box_reg: 0.2271 (0.2345)  loss_objectness: 0.0817 (0.0841)  loss_rpn_box_reg: 0.0856 (0.0752)  time: 0.2218  data: 0.0061  max mem: 9052\n",
      "Epoch: [2]  [4200/7330]  eta: 0:11:26  lr: 0.000008  loss: 0.5894 (0.6598)  loss_classifier: 0.2483 (0.2662)  loss_box_reg: 0.2223 (0.2345)  loss_objectness: 0.0806 (0.0840)  loss_rpn_box_reg: 0.0662 (0.0752)  time: 0.2215  data: 0.0065  max mem: 9052\n",
      "Epoch: [2]  [4300/7330]  eta: 0:11:04  lr: 0.000008  loss: 0.6460 (0.6602)  loss_classifier: 0.2560 (0.2663)  loss_box_reg: 0.2324 (0.2346)  loss_objectness: 0.0819 (0.0841)  loss_rpn_box_reg: 0.0786 (0.0752)  time: 0.2190  data: 0.0068  max mem: 9052\n",
      "Epoch: [2]  [4400/7330]  eta: 0:10:43  lr: 0.000008  loss: 0.6239 (0.6599)  loss_classifier: 0.2462 (0.2662)  loss_box_reg: 0.2164 (0.2345)  loss_objectness: 0.0766 (0.0840)  loss_rpn_box_reg: 0.0672 (0.0752)  time: 0.2247  data: 0.0061  max mem: 9052\n",
      "Epoch: [2]  [4500/7330]  eta: 0:10:21  lr: 0.000008  loss: 0.6161 (0.6593)  loss_classifier: 0.2506 (0.2660)  loss_box_reg: 0.2243 (0.2343)  loss_objectness: 0.0729 (0.0839)  loss_rpn_box_reg: 0.0735 (0.0751)  time: 0.2240  data: 0.0061  max mem: 9052\n",
      "Epoch: [2]  [4600/7330]  eta: 0:09:59  lr: 0.000008  loss: 0.6445 (0.6590)  loss_classifier: 0.2689 (0.2659)  loss_box_reg: 0.2483 (0.2343)  loss_objectness: 0.0826 (0.0839)  loss_rpn_box_reg: 0.0605 (0.0750)  time: 0.2172  data: 0.0060  max mem: 9052\n",
      "Epoch: [2]  [4700/7330]  eta: 0:09:37  lr: 0.000008  loss: 0.6581 (0.6588)  loss_classifier: 0.2628 (0.2658)  loss_box_reg: 0.2230 (0.2342)  loss_objectness: 0.0806 (0.0838)  loss_rpn_box_reg: 0.0655 (0.0750)  time: 0.2201  data: 0.0059  max mem: 9052\n",
      "Epoch: [2]  [4800/7330]  eta: 0:09:15  lr: 0.000008  loss: 0.6405 (0.6587)  loss_classifier: 0.2565 (0.2658)  loss_box_reg: 0.2291 (0.2342)  loss_objectness: 0.0785 (0.0838)  loss_rpn_box_reg: 0.0643 (0.0749)  time: 0.2128  data: 0.0062  max mem: 9052\n",
      "Epoch: [2]  [4900/7330]  eta: 0:08:53  lr: 0.000008  loss: 0.6845 (0.6591)  loss_classifier: 0.2682 (0.2658)  loss_box_reg: 0.2495 (0.2343)  loss_objectness: 0.0823 (0.0839)  loss_rpn_box_reg: 0.0812 (0.0750)  time: 0.2214  data: 0.0062  max mem: 9052\n",
      "Epoch: [2]  [5000/7330]  eta: 0:08:31  lr: 0.000008  loss: 0.6641 (0.6587)  loss_classifier: 0.2677 (0.2657)  loss_box_reg: 0.2300 (0.2342)  loss_objectness: 0.0754 (0.0839)  loss_rpn_box_reg: 0.0654 (0.0749)  time: 0.2216  data: 0.0057  max mem: 9052\n",
      "Epoch: [2]  [5100/7330]  eta: 0:08:09  lr: 0.000007  loss: 0.6443 (0.6587)  loss_classifier: 0.2598 (0.2657)  loss_box_reg: 0.2193 (0.2342)  loss_objectness: 0.0805 (0.0839)  loss_rpn_box_reg: 0.0803 (0.0749)  time: 0.2124  data: 0.0060  max mem: 9052\n",
      "Epoch: [2]  [5200/7330]  eta: 0:07:47  lr: 0.000007  loss: 0.6299 (0.6586)  loss_classifier: 0.2545 (0.2657)  loss_box_reg: 0.2312 (0.2341)  loss_objectness: 0.0781 (0.0839)  loss_rpn_box_reg: 0.0635 (0.0749)  time: 0.2197  data: 0.0061  max mem: 9052\n",
      "Epoch: [2]  [5300/7330]  eta: 0:07:25  lr: 0.000007  loss: 0.7010 (0.6584)  loss_classifier: 0.2771 (0.2656)  loss_box_reg: 0.2329 (0.2341)  loss_objectness: 0.0723 (0.0838)  loss_rpn_box_reg: 0.0620 (0.0750)  time: 0.2142  data: 0.0060  max mem: 9052\n",
      "Epoch: [2]  [5400/7330]  eta: 0:07:03  lr: 0.000007  loss: 0.6760 (0.6582)  loss_classifier: 0.2476 (0.2655)  loss_box_reg: 0.2217 (0.2340)  loss_objectness: 0.0821 (0.0838)  loss_rpn_box_reg: 0.0738 (0.0750)  time: 0.2156  data: 0.0065  max mem: 9052\n",
      "Epoch: [2]  [5500/7330]  eta: 0:06:41  lr: 0.000007  loss: 0.6555 (0.6580)  loss_classifier: 0.2617 (0.2654)  loss_box_reg: 0.2434 (0.2340)  loss_objectness: 0.0767 (0.0837)  loss_rpn_box_reg: 0.0794 (0.0749)  time: 0.2201  data: 0.0061  max mem: 9052\n",
      "Epoch: [2]  [5600/7330]  eta: 0:06:19  lr: 0.000007  loss: 0.6200 (0.6578)  loss_classifier: 0.2394 (0.2653)  loss_box_reg: 0.2286 (0.2340)  loss_objectness: 0.0813 (0.0837)  loss_rpn_box_reg: 0.0684 (0.0748)  time: 0.2205  data: 0.0062  max mem: 9052\n",
      "Epoch: [2]  [5700/7330]  eta: 0:05:57  lr: 0.000007  loss: 0.6629 (0.6575)  loss_classifier: 0.2675 (0.2652)  loss_box_reg: 0.2347 (0.2339)  loss_objectness: 0.0841 (0.0837)  loss_rpn_box_reg: 0.0797 (0.0747)  time: 0.2176  data: 0.0059  max mem: 9052\n",
      "Epoch: [2]  [5800/7330]  eta: 0:05:35  lr: 0.000007  loss: 0.6842 (0.6577)  loss_classifier: 0.2741 (0.2652)  loss_box_reg: 0.2484 (0.2340)  loss_objectness: 0.0905 (0.0837)  loss_rpn_box_reg: 0.0807 (0.0748)  time: 0.2234  data: 0.0059  max mem: 9052\n",
      "Epoch: [2]  [5900/7330]  eta: 0:05:13  lr: 0.000007  loss: 0.6439 (0.6573)  loss_classifier: 0.2673 (0.2651)  loss_box_reg: 0.2257 (0.2340)  loss_objectness: 0.0755 (0.0836)  loss_rpn_box_reg: 0.0670 (0.0747)  time: 0.2154  data: 0.0061  max mem: 9052\n",
      "Epoch: [2]  [6000/7330]  eta: 0:04:51  lr: 0.000007  loss: 0.6441 (0.6571)  loss_classifier: 0.2603 (0.2650)  loss_box_reg: 0.2402 (0.2339)  loss_objectness: 0.0721 (0.0836)  loss_rpn_box_reg: 0.0678 (0.0746)  time: 0.2169  data: 0.0060  max mem: 9052\n",
      "Epoch: [2]  [6100/7330]  eta: 0:04:29  lr: 0.000007  loss: 0.6323 (0.6572)  loss_classifier: 0.2499 (0.2650)  loss_box_reg: 0.2243 (0.2340)  loss_objectness: 0.0780 (0.0836)  loss_rpn_box_reg: 0.0654 (0.0746)  time: 0.2155  data: 0.0060  max mem: 9052\n",
      "Epoch: [2]  [6200/7330]  eta: 0:04:07  lr: 0.000007  loss: 0.6430 (0.6568)  loss_classifier: 0.2503 (0.2649)  loss_box_reg: 0.2261 (0.2338)  loss_objectness: 0.0805 (0.0835)  loss_rpn_box_reg: 0.0796 (0.0746)  time: 0.2193  data: 0.0065  max mem: 9052\n",
      "Epoch: [2]  [6300/7330]  eta: 0:03:45  lr: 0.000007  loss: 0.6755 (0.6568)  loss_classifier: 0.2702 (0.2649)  loss_box_reg: 0.2353 (0.2338)  loss_objectness: 0.0770 (0.0835)  loss_rpn_box_reg: 0.0691 (0.0746)  time: 0.2206  data: 0.0061  max mem: 9052\n",
      "Epoch: [2]  [6400/7330]  eta: 0:03:23  lr: 0.000007  loss: 0.6414 (0.6568)  loss_classifier: 0.2646 (0.2649)  loss_box_reg: 0.2354 (0.2338)  loss_objectness: 0.0783 (0.0835)  loss_rpn_box_reg: 0.0749 (0.0746)  time: 0.2163  data: 0.0059  max mem: 9052\n",
      "Epoch: [2]  [6500/7330]  eta: 0:03:02  lr: 0.000007  loss: 0.6849 (0.6567)  loss_classifier: 0.2677 (0.2648)  loss_box_reg: 0.2267 (0.2338)  loss_objectness: 0.0752 (0.0834)  loss_rpn_box_reg: 0.0690 (0.0747)  time: 0.2214  data: 0.0059  max mem: 9052\n",
      "Epoch: [2]  [6600/7330]  eta: 0:02:40  lr: 0.000007  loss: 0.6629 (0.6567)  loss_classifier: 0.2634 (0.2648)  loss_box_reg: 0.2422 (0.2338)  loss_objectness: 0.0816 (0.0834)  loss_rpn_box_reg: 0.0806 (0.0748)  time: 0.2186  data: 0.0062  max mem: 9052\n",
      "Epoch: [2]  [6700/7330]  eta: 0:02:18  lr: 0.000007  loss: 0.6395 (0.6562)  loss_classifier: 0.2614 (0.2646)  loss_box_reg: 0.2314 (0.2336)  loss_objectness: 0.0749 (0.0833)  loss_rpn_box_reg: 0.0686 (0.0747)  time: 0.2271  data: 0.0061  max mem: 9052\n",
      "Epoch: [2]  [6800/7330]  eta: 0:01:56  lr: 0.000007  loss: 0.6704 (0.6563)  loss_classifier: 0.2750 (0.2646)  loss_box_reg: 0.2440 (0.2337)  loss_objectness: 0.0805 (0.0833)  loss_rpn_box_reg: 0.0678 (0.0747)  time: 0.2219  data: 0.0060  max mem: 9052\n",
      "Epoch: [2]  [6900/7330]  eta: 0:01:34  lr: 0.000007  loss: 0.6520 (0.6562)  loss_classifier: 0.2667 (0.2645)  loss_box_reg: 0.2210 (0.2336)  loss_objectness: 0.0845 (0.0833)  loss_rpn_box_reg: 0.0654 (0.0747)  time: 0.2149  data: 0.0059  max mem: 9052\n",
      "Epoch: [2]  [7000/7330]  eta: 0:01:12  lr: 0.000007  loss: 0.6567 (0.6559)  loss_classifier: 0.2535 (0.2644)  loss_box_reg: 0.2248 (0.2335)  loss_objectness: 0.0781 (0.0832)  loss_rpn_box_reg: 0.0686 (0.0747)  time: 0.2222  data: 0.0062  max mem: 9052\n",
      "Epoch: [2]  [7100/7330]  eta: 0:00:50  lr: 0.000007  loss: 0.6613 (0.6558)  loss_classifier: 0.2732 (0.2644)  loss_box_reg: 0.2503 (0.2335)  loss_objectness: 0.0776 (0.0832)  loss_rpn_box_reg: 0.0742 (0.0747)  time: 0.2233  data: 0.0059  max mem: 9052\n",
      "Epoch: [2]  [7200/7330]  eta: 0:00:28  lr: 0.000007  loss: 0.6048 (0.6556)  loss_classifier: 0.2462 (0.2643)  loss_box_reg: 0.2177 (0.2335)  loss_objectness: 0.0745 (0.0832)  loss_rpn_box_reg: 0.0623 (0.0746)  time: 0.2188  data: 0.0059  max mem: 9052\n",
      "Epoch: [2]  [7300/7330]  eta: 0:00:06  lr: 0.000007  loss: 0.6709 (0.6555)  loss_classifier: 0.2648 (0.2642)  loss_box_reg: 0.2444 (0.2334)  loss_objectness: 0.0836 (0.0831)  loss_rpn_box_reg: 0.0844 (0.0747)  time: 0.2170  data: 0.0060  max mem: 9052\n",
      "Epoch: [2]  [7329/7330]  eta: 0:00:00  lr: 0.000007  loss: 0.6441 (0.6555)  loss_classifier: 0.2547 (0.2642)  loss_box_reg: 0.2314 (0.2334)  loss_objectness: 0.0820 (0.0831)  loss_rpn_box_reg: 0.0797 (0.0747)  time: 0.2113  data: 0.0061  max mem: 9052\n",
      "Epoch: [2] Total time: 0:26:46 (0.2192 s / it)\n",
      "Test:  [  0/313]  eta: 0:02:27  model_time: 0.1372 (0.1372)  evaluator_time: 0.0261 (0.0261)  time: 0.4725  data: 0.3005  max mem: 9052\n",
      "Test:  [312/313]  eta: 0:00:00  model_time: 0.1292 (0.1302)  evaluator_time: 0.0177 (0.0245)  time: 0.1579  data: 0.0064  max mem: 9052\n",
      "Test: Total time: 0:00:52 (0.1687 s / it)\n",
      "Averaged stats: model_time: 0.1292 (0.1302)  evaluator_time: 0.0177 (0.0245)\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "IoU metric: bbox\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.315\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.574\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.346\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.039\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.416\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.551\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.250\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.308\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.321\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.046\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.417\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.563\n",
      "Epoch: [3]  [   0/7330]  eta: 1:36:04  lr: 0.000007  loss: 0.5460 (0.5460)  loss_classifier: 0.2200 (0.2200)  loss_box_reg: 0.2281 (0.2281)  loss_objectness: 0.0601 (0.0601)  loss_rpn_box_reg: 0.0379 (0.0379)  time: 0.7864  data: 0.5562  max mem: 9052\n",
      "Epoch: [3]  [ 100/7330]  eta: 0:27:01  lr: 0.000007  loss: 0.6314 (0.6392)  loss_classifier: 0.2511 (0.2571)  loss_box_reg: 0.2350 (0.2278)  loss_objectness: 0.0717 (0.0813)  loss_rpn_box_reg: 0.0655 (0.0730)  time: 0.2191  data: 0.0066  max mem: 9052\n",
      "Epoch: [3]  [ 200/7330]  eta: 0:26:24  lr: 0.000007  loss: 0.6612 (0.6501)  loss_classifier: 0.2653 (0.2615)  loss_box_reg: 0.2414 (0.2324)  loss_objectness: 0.0770 (0.0815)  loss_rpn_box_reg: 0.0766 (0.0747)  time: 0.2226  data: 0.0063  max mem: 9052\n",
      "Epoch: [3]  [ 300/7330]  eta: 0:25:55  lr: 0.000007  loss: 0.6277 (0.6465)  loss_classifier: 0.2510 (0.2604)  loss_box_reg: 0.2255 (0.2301)  loss_objectness: 0.0809 (0.0818)  loss_rpn_box_reg: 0.0731 (0.0743)  time: 0.2176  data: 0.0064  max mem: 9052\n",
      "Epoch: [3]  [ 400/7330]  eta: 0:25:24  lr: 0.000007  loss: 0.6545 (0.6522)  loss_classifier: 0.2709 (0.2622)  loss_box_reg: 0.2393 (0.2321)  loss_objectness: 0.0854 (0.0830)  loss_rpn_box_reg: 0.0642 (0.0749)  time: 0.2091  data: 0.0064  max mem: 9052\n",
      "Epoch: [3]  [ 500/7330]  eta: 0:25:01  lr: 0.000007  loss: 0.6721 (0.6498)  loss_classifier: 0.2733 (0.2614)  loss_box_reg: 0.2439 (0.2314)  loss_objectness: 0.0786 (0.0823)  loss_rpn_box_reg: 0.0773 (0.0748)  time: 0.2223  data: 0.0064  max mem: 9052\n",
      "Epoch: [3]  [ 600/7330]  eta: 0:24:40  lr: 0.000007  loss: 0.6502 (0.6496)  loss_classifier: 0.2627 (0.2615)  loss_box_reg: 0.2329 (0.2313)  loss_objectness: 0.0786 (0.0816)  loss_rpn_box_reg: 0.0773 (0.0752)  time: 0.2228  data: 0.0062  max mem: 9052\n",
      "Epoch: [3]  [ 700/7330]  eta: 0:24:19  lr: 0.000007  loss: 0.6049 (0.6500)  loss_classifier: 0.2480 (0.2617)  loss_box_reg: 0.2270 (0.2321)  loss_objectness: 0.0793 (0.0815)  loss_rpn_box_reg: 0.0583 (0.0748)  time: 0.2187  data: 0.0064  max mem: 9052\n",
      "Epoch: [3]  [ 800/7330]  eta: 0:23:55  lr: 0.000007  loss: 0.5882 (0.6490)  loss_classifier: 0.2487 (0.2612)  loss_box_reg: 0.2290 (0.2320)  loss_objectness: 0.0773 (0.0813)  loss_rpn_box_reg: 0.0684 (0.0745)  time: 0.2191  data: 0.0063  max mem: 9052\n",
      "Epoch: [3]  [ 900/7330]  eta: 0:23:33  lr: 0.000007  loss: 0.5893 (0.6476)  loss_classifier: 0.2549 (0.2606)  loss_box_reg: 0.2016 (0.2315)  loss_objectness: 0.0714 (0.0809)  loss_rpn_box_reg: 0.0658 (0.0746)  time: 0.2187  data: 0.0062  max mem: 9052\n",
      "Epoch: [3]  [1000/7330]  eta: 0:23:11  lr: 0.000007  loss: 0.6097 (0.6463)  loss_classifier: 0.2560 (0.2602)  loss_box_reg: 0.2098 (0.2309)  loss_objectness: 0.0749 (0.0806)  loss_rpn_box_reg: 0.0709 (0.0745)  time: 0.2203  data: 0.0063  max mem: 9052\n",
      "Epoch: [3]  [1100/7330]  eta: 0:22:48  lr: 0.000007  loss: 0.6370 (0.6447)  loss_classifier: 0.2507 (0.2595)  loss_box_reg: 0.2395 (0.2305)  loss_objectness: 0.0836 (0.0806)  loss_rpn_box_reg: 0.0664 (0.0741)  time: 0.2161  data: 0.0062  max mem: 9052\n",
      "Epoch: [3]  [1200/7330]  eta: 0:22:25  lr: 0.000007  loss: 0.6651 (0.6446)  loss_classifier: 0.2638 (0.2594)  loss_box_reg: 0.2385 (0.2306)  loss_objectness: 0.0736 (0.0804)  loss_rpn_box_reg: 0.0803 (0.0742)  time: 0.2129  data: 0.0064  max mem: 9052\n",
      "Epoch: [3]  [1300/7330]  eta: 0:22:03  lr: 0.000007  loss: 0.6124 (0.6440)  loss_classifier: 0.2436 (0.2590)  loss_box_reg: 0.2211 (0.2303)  loss_objectness: 0.0732 (0.0805)  loss_rpn_box_reg: 0.0683 (0.0741)  time: 0.2215  data: 0.0063  max mem: 9052\n",
      "Epoch: [3]  [1400/7330]  eta: 0:21:42  lr: 0.000007  loss: 0.6315 (0.6423)  loss_classifier: 0.2495 (0.2583)  loss_box_reg: 0.2230 (0.2300)  loss_objectness: 0.0746 (0.0803)  loss_rpn_box_reg: 0.0678 (0.0736)  time: 0.2221  data: 0.0065  max mem: 9052\n",
      "Epoch: [3]  [1500/7330]  eta: 0:21:21  lr: 0.000006  loss: 0.6205 (0.6412)  loss_classifier: 0.2595 (0.2579)  loss_box_reg: 0.2206 (0.2295)  loss_objectness: 0.0856 (0.0801)  loss_rpn_box_reg: 0.0715 (0.0737)  time: 0.2246  data: 0.0066  max mem: 9052\n",
      "Epoch: [3]  [1600/7330]  eta: 0:21:00  lr: 0.000006  loss: 0.6288 (0.6411)  loss_classifier: 0.2490 (0.2579)  loss_box_reg: 0.2228 (0.2296)  loss_objectness: 0.0778 (0.0800)  loss_rpn_box_reg: 0.0668 (0.0736)  time: 0.2195  data: 0.0065  max mem: 9052\n",
      "Epoch: [3]  [1700/7330]  eta: 0:20:38  lr: 0.000006  loss: 0.6000 (0.6412)  loss_classifier: 0.2430 (0.2579)  loss_box_reg: 0.2279 (0.2295)  loss_objectness: 0.0771 (0.0800)  loss_rpn_box_reg: 0.0525 (0.0736)  time: 0.2227  data: 0.0065  max mem: 9052\n",
      "Epoch: [3]  [1800/7330]  eta: 0:20:17  lr: 0.000006  loss: 0.6249 (0.6413)  loss_classifier: 0.2534 (0.2580)  loss_box_reg: 0.2288 (0.2295)  loss_objectness: 0.0689 (0.0801)  loss_rpn_box_reg: 0.0697 (0.0737)  time: 0.2277  data: 0.0064  max mem: 9052\n",
      "Epoch: [3]  [1900/7330]  eta: 0:19:54  lr: 0.000006  loss: 0.6674 (0.6424)  loss_classifier: 0.2595 (0.2583)  loss_box_reg: 0.2349 (0.2298)  loss_objectness: 0.0752 (0.0803)  loss_rpn_box_reg: 0.0846 (0.0740)  time: 0.2238  data: 0.0067  max mem: 9052\n",
      "Epoch: [3]  [2000/7330]  eta: 0:19:33  lr: 0.000006  loss: 0.6571 (0.6431)  loss_classifier: 0.2625 (0.2585)  loss_box_reg: 0.2486 (0.2303)  loss_objectness: 0.0830 (0.0804)  loss_rpn_box_reg: 0.0660 (0.0739)  time: 0.2258  data: 0.0069  max mem: 9052\n",
      "Epoch: [3]  [2100/7330]  eta: 0:19:11  lr: 0.000006  loss: 0.6103 (0.6436)  loss_classifier: 0.2444 (0.2587)  loss_box_reg: 0.2182 (0.2305)  loss_objectness: 0.0741 (0.0804)  loss_rpn_box_reg: 0.0778 (0.0740)  time: 0.2290  data: 0.0073  max mem: 9052\n",
      "Epoch: [3]  [2200/7330]  eta: 0:18:50  lr: 0.000006  loss: 0.6028 (0.6437)  loss_classifier: 0.2395 (0.2587)  loss_box_reg: 0.2187 (0.2305)  loss_objectness: 0.0847 (0.0805)  loss_rpn_box_reg: 0.0756 (0.0740)  time: 0.2200  data: 0.0071  max mem: 9052\n",
      "Epoch: [3]  [2300/7330]  eta: 0:18:28  lr: 0.000006  loss: 0.6047 (0.6429)  loss_classifier: 0.2362 (0.2584)  loss_box_reg: 0.2308 (0.2304)  loss_objectness: 0.0738 (0.0802)  loss_rpn_box_reg: 0.0645 (0.0738)  time: 0.2218  data: 0.0063  max mem: 9052\n",
      "Epoch: [3]  [2400/7330]  eta: 0:18:06  lr: 0.000006  loss: 0.6329 (0.6430)  loss_classifier: 0.2598 (0.2586)  loss_box_reg: 0.2245 (0.2305)  loss_objectness: 0.0819 (0.0801)  loss_rpn_box_reg: 0.0718 (0.0738)  time: 0.2156  data: 0.0064  max mem: 9052\n",
      "Epoch: [3]  [2500/7330]  eta: 0:17:44  lr: 0.000006  loss: 0.6693 (0.6436)  loss_classifier: 0.2667 (0.2589)  loss_box_reg: 0.2475 (0.2308)  loss_objectness: 0.0804 (0.0800)  loss_rpn_box_reg: 0.0651 (0.0738)  time: 0.2219  data: 0.0066  max mem: 9052\n",
      "Epoch: [3]  [2600/7330]  eta: 0:17:22  lr: 0.000006  loss: 0.6324 (0.6435)  loss_classifier: 0.2559 (0.2590)  loss_box_reg: 0.2113 (0.2308)  loss_objectness: 0.0753 (0.0800)  loss_rpn_box_reg: 0.0729 (0.0737)  time: 0.2229  data: 0.0064  max mem: 9052\n",
      "Epoch: [3]  [2700/7330]  eta: 0:16:59  lr: 0.000006  loss: 0.5946 (0.6433)  loss_classifier: 0.2367 (0.2589)  loss_box_reg: 0.2050 (0.2307)  loss_objectness: 0.0746 (0.0800)  loss_rpn_box_reg: 0.0734 (0.0736)  time: 0.2122  data: 0.0063  max mem: 9052\n",
      "Epoch: [3]  [2800/7330]  eta: 0:16:37  lr: 0.000006  loss: 0.6234 (0.6426)  loss_classifier: 0.2531 (0.2586)  loss_box_reg: 0.2259 (0.2305)  loss_objectness: 0.0728 (0.0799)  loss_rpn_box_reg: 0.0665 (0.0736)  time: 0.2185  data: 0.0065  max mem: 9052\n",
      "Epoch: [3]  [2900/7330]  eta: 0:16:15  lr: 0.000006  loss: 0.6566 (0.6427)  loss_classifier: 0.2714 (0.2587)  loss_box_reg: 0.2376 (0.2306)  loss_objectness: 0.0692 (0.0798)  loss_rpn_box_reg: 0.0651 (0.0736)  time: 0.2206  data: 0.0068  max mem: 9052\n",
      "Epoch: [3]  [3000/7330]  eta: 0:15:53  lr: 0.000006  loss: 0.6711 (0.6422)  loss_classifier: 0.2513 (0.2585)  loss_box_reg: 0.2370 (0.2303)  loss_objectness: 0.0724 (0.0798)  loss_rpn_box_reg: 0.0863 (0.0736)  time: 0.2175  data: 0.0066  max mem: 9052\n",
      "Epoch: [3]  [3100/7330]  eta: 0:15:31  lr: 0.000006  loss: 0.5978 (0.6422)  loss_classifier: 0.2414 (0.2585)  loss_box_reg: 0.2081 (0.2303)  loss_objectness: 0.0786 (0.0798)  loss_rpn_box_reg: 0.0809 (0.0736)  time: 0.2157  data: 0.0063  max mem: 9052\n",
      "Epoch: [3]  [3200/7330]  eta: 0:15:09  lr: 0.000006  loss: 0.6315 (0.6423)  loss_classifier: 0.2563 (0.2585)  loss_box_reg: 0.2256 (0.2303)  loss_objectness: 0.0793 (0.0798)  loss_rpn_box_reg: 0.0637 (0.0737)  time: 0.2151  data: 0.0065  max mem: 9052\n",
      "Epoch: [3]  [3300/7330]  eta: 0:14:47  lr: 0.000006  loss: 0.6438 (0.6426)  loss_classifier: 0.2518 (0.2585)  loss_box_reg: 0.2246 (0.2304)  loss_objectness: 0.0734 (0.0799)  loss_rpn_box_reg: 0.0865 (0.0738)  time: 0.2241  data: 0.0063  max mem: 9052\n",
      "Epoch: [3]  [3400/7330]  eta: 0:14:25  lr: 0.000006  loss: 0.6653 (0.6426)  loss_classifier: 0.2745 (0.2586)  loss_box_reg: 0.2365 (0.2303)  loss_objectness: 0.0827 (0.0799)  loss_rpn_box_reg: 0.0682 (0.0738)  time: 0.2245  data: 0.0063  max mem: 9052\n",
      "Epoch: [3]  [3500/7330]  eta: 0:14:03  lr: 0.000006  loss: 0.6617 (0.6423)  loss_classifier: 0.2489 (0.2583)  loss_box_reg: 0.2259 (0.2302)  loss_objectness: 0.0836 (0.0799)  loss_rpn_box_reg: 0.0773 (0.0739)  time: 0.2200  data: 0.0063  max mem: 9052\n",
      "Epoch: [3]  [3600/7330]  eta: 0:13:41  lr: 0.000006  loss: 0.6461 (0.6421)  loss_classifier: 0.2501 (0.2582)  loss_box_reg: 0.2294 (0.2302)  loss_objectness: 0.0798 (0.0799)  loss_rpn_box_reg: 0.0648 (0.0738)  time: 0.2192  data: 0.0067  max mem: 9052\n",
      "Epoch: [3]  [3700/7330]  eta: 0:13:19  lr: 0.000006  loss: 0.6165 (0.6418)  loss_classifier: 0.2445 (0.2581)  loss_box_reg: 0.2186 (0.2301)  loss_objectness: 0.0774 (0.0798)  loss_rpn_box_reg: 0.0724 (0.0738)  time: 0.2215  data: 0.0064  max mem: 9052\n",
      "Epoch: [3]  [3800/7330]  eta: 0:12:57  lr: 0.000006  loss: 0.6819 (0.6417)  loss_classifier: 0.2765 (0.2580)  loss_box_reg: 0.2341 (0.2300)  loss_objectness: 0.0799 (0.0798)  loss_rpn_box_reg: 0.0745 (0.0738)  time: 0.2195  data: 0.0065  max mem: 9052\n",
      "Epoch: [3]  [3900/7330]  eta: 0:12:35  lr: 0.000006  loss: 0.6421 (0.6419)  loss_classifier: 0.2581 (0.2581)  loss_box_reg: 0.2346 (0.2301)  loss_objectness: 0.0710 (0.0798)  loss_rpn_box_reg: 0.0677 (0.0738)  time: 0.2215  data: 0.0063  max mem: 9052\n",
      "Epoch: [3]  [4000/7330]  eta: 0:12:13  lr: 0.000006  loss: 0.6144 (0.6418)  loss_classifier: 0.2433 (0.2581)  loss_box_reg: 0.2258 (0.2301)  loss_objectness: 0.0698 (0.0798)  loss_rpn_box_reg: 0.0791 (0.0738)  time: 0.2180  data: 0.0063  max mem: 9052\n",
      "Epoch: [3]  [4100/7330]  eta: 0:11:51  lr: 0.000006  loss: 0.6500 (0.6417)  loss_classifier: 0.2588 (0.2580)  loss_box_reg: 0.2193 (0.2300)  loss_objectness: 0.0849 (0.0798)  loss_rpn_box_reg: 0.0731 (0.0738)  time: 0.2195  data: 0.0062  max mem: 9052\n",
      "Epoch: [3]  [4200/7330]  eta: 0:11:29  lr: 0.000006  loss: 0.6079 (0.6412)  loss_classifier: 0.2450 (0.2578)  loss_box_reg: 0.2341 (0.2299)  loss_objectness: 0.0713 (0.0797)  loss_rpn_box_reg: 0.0706 (0.0737)  time: 0.2238  data: 0.0062  max mem: 9052\n",
      "Epoch: [3]  [4300/7330]  eta: 0:11:07  lr: 0.000006  loss: 0.6398 (0.6411)  loss_classifier: 0.2607 (0.2577)  loss_box_reg: 0.2190 (0.2299)  loss_objectness: 0.0790 (0.0797)  loss_rpn_box_reg: 0.0692 (0.0737)  time: 0.2261  data: 0.0067  max mem: 9052\n",
      "Epoch: [3]  [4400/7330]  eta: 0:10:45  lr: 0.000006  loss: 0.6454 (0.6411)  loss_classifier: 0.2550 (0.2577)  loss_box_reg: 0.2249 (0.2299)  loss_objectness: 0.0768 (0.0798)  loss_rpn_box_reg: 0.0763 (0.0737)  time: 0.2219  data: 0.0063  max mem: 9052\n",
      "Epoch: [3]  [4500/7330]  eta: 0:10:23  lr: 0.000006  loss: 0.6358 (0.6411)  loss_classifier: 0.2623 (0.2577)  loss_box_reg: 0.2286 (0.2299)  loss_objectness: 0.0734 (0.0798)  loss_rpn_box_reg: 0.0735 (0.0737)  time: 0.2213  data: 0.0066  max mem: 9052\n",
      "Epoch: [3]  [4600/7330]  eta: 0:10:01  lr: 0.000006  loss: 0.6256 (0.6407)  loss_classifier: 0.2380 (0.2575)  loss_box_reg: 0.2161 (0.2297)  loss_objectness: 0.0675 (0.0797)  loss_rpn_box_reg: 0.0612 (0.0737)  time: 0.2262  data: 0.0061  max mem: 9052\n",
      "Epoch: [3]  [4700/7330]  eta: 0:09:39  lr: 0.000006  loss: 0.6436 (0.6406)  loss_classifier: 0.2496 (0.2575)  loss_box_reg: 0.2349 (0.2298)  loss_objectness: 0.0730 (0.0797)  loss_rpn_box_reg: 0.0813 (0.0736)  time: 0.2222  data: 0.0064  max mem: 9052\n",
      "Epoch: [3]  [4800/7330]  eta: 0:09:17  lr: 0.000006  loss: 0.6018 (0.6401)  loss_classifier: 0.2380 (0.2573)  loss_box_reg: 0.2163 (0.2296)  loss_objectness: 0.0704 (0.0796)  loss_rpn_box_reg: 0.0695 (0.0736)  time: 0.2230  data: 0.0064  max mem: 9052\n",
      "Epoch: [3]  [4900/7330]  eta: 0:08:55  lr: 0.000006  loss: 0.6446 (0.6402)  loss_classifier: 0.2623 (0.2573)  loss_box_reg: 0.2216 (0.2296)  loss_objectness: 0.0805 (0.0796)  loss_rpn_box_reg: 0.0710 (0.0736)  time: 0.2257  data: 0.0067  max mem: 9052\n",
      "Epoch: [3]  [5000/7330]  eta: 0:08:33  lr: 0.000006  loss: 0.6218 (0.6403)  loss_classifier: 0.2652 (0.2574)  loss_box_reg: 0.2306 (0.2297)  loss_objectness: 0.0716 (0.0796)  loss_rpn_box_reg: 0.0736 (0.0735)  time: 0.2228  data: 0.0064  max mem: 9052\n",
      "Epoch: [3]  [5100/7330]  eta: 0:08:11  lr: 0.000005  loss: 0.6137 (0.6402)  loss_classifier: 0.2382 (0.2574)  loss_box_reg: 0.2130 (0.2297)  loss_objectness: 0.0759 (0.0796)  loss_rpn_box_reg: 0.0802 (0.0735)  time: 0.2240  data: 0.0077  max mem: 9052\n",
      "Epoch: [3]  [5200/7330]  eta: 0:07:49  lr: 0.000005  loss: 0.6323 (0.6402)  loss_classifier: 0.2509 (0.2574)  loss_box_reg: 0.2227 (0.2296)  loss_objectness: 0.0858 (0.0796)  loss_rpn_box_reg: 0.0732 (0.0735)  time: 0.2224  data: 0.0065  max mem: 9052\n",
      "Epoch: [3]  [5300/7330]  eta: 0:07:27  lr: 0.000005  loss: 0.6331 (0.6401)  loss_classifier: 0.2577 (0.2574)  loss_box_reg: 0.2160 (0.2296)  loss_objectness: 0.0751 (0.0796)  loss_rpn_box_reg: 0.0710 (0.0735)  time: 0.2219  data: 0.0066  max mem: 9052\n",
      "Epoch: [3]  [5400/7330]  eta: 0:07:05  lr: 0.000005  loss: 0.5629 (0.6398)  loss_classifier: 0.2346 (0.2573)  loss_box_reg: 0.1922 (0.2295)  loss_objectness: 0.0684 (0.0795)  loss_rpn_box_reg: 0.0636 (0.0735)  time: 0.2209  data: 0.0065  max mem: 9052\n",
      "Epoch: [3]  [5500/7330]  eta: 0:06:43  lr: 0.000005  loss: 0.6523 (0.6396)  loss_classifier: 0.2510 (0.2572)  loss_box_reg: 0.2359 (0.2295)  loss_objectness: 0.0694 (0.0795)  loss_rpn_box_reg: 0.0748 (0.0735)  time: 0.2205  data: 0.0065  max mem: 9052\n",
      "Epoch: [3]  [5600/7330]  eta: 0:06:21  lr: 0.000005  loss: 0.6118 (0.6391)  loss_classifier: 0.2426 (0.2569)  loss_box_reg: 0.2190 (0.2293)  loss_objectness: 0.0757 (0.0794)  loss_rpn_box_reg: 0.0653 (0.0733)  time: 0.2233  data: 0.0063  max mem: 9052\n",
      "Epoch: [3]  [5700/7330]  eta: 0:05:59  lr: 0.000005  loss: 0.6428 (0.6388)  loss_classifier: 0.2524 (0.2568)  loss_box_reg: 0.2278 (0.2292)  loss_objectness: 0.0746 (0.0794)  loss_rpn_box_reg: 0.0755 (0.0733)  time: 0.2217  data: 0.0064  max mem: 9052\n",
      "Epoch: [3]  [5800/7330]  eta: 0:05:37  lr: 0.000005  loss: 0.6010 (0.6388)  loss_classifier: 0.2403 (0.2569)  loss_box_reg: 0.2191 (0.2293)  loss_objectness: 0.0681 (0.0794)  loss_rpn_box_reg: 0.0637 (0.0733)  time: 0.2233  data: 0.0065  max mem: 9052\n",
      "Epoch: [3]  [5900/7330]  eta: 0:05:15  lr: 0.000005  loss: 0.6353 (0.6391)  loss_classifier: 0.2631 (0.2570)  loss_box_reg: 0.2216 (0.2294)  loss_objectness: 0.0780 (0.0794)  loss_rpn_box_reg: 0.0726 (0.0732)  time: 0.2223  data: 0.0065  max mem: 9052\n",
      "Epoch: [3]  [6000/7330]  eta: 0:04:53  lr: 0.000005  loss: 0.5841 (0.6386)  loss_classifier: 0.2350 (0.2569)  loss_box_reg: 0.2192 (0.2293)  loss_objectness: 0.0765 (0.0793)  loss_rpn_box_reg: 0.0605 (0.0731)  time: 0.2219  data: 0.0061  max mem: 9052\n",
      "Epoch: [3]  [6100/7330]  eta: 0:04:31  lr: 0.000005  loss: 0.6287 (0.6386)  loss_classifier: 0.2531 (0.2568)  loss_box_reg: 0.2284 (0.2294)  loss_objectness: 0.0817 (0.0793)  loss_rpn_box_reg: 0.0627 (0.0731)  time: 0.2215  data: 0.0064  max mem: 9052\n",
      "Epoch: [3]  [6200/7330]  eta: 0:04:09  lr: 0.000005  loss: 0.6393 (0.6388)  loss_classifier: 0.2540 (0.2569)  loss_box_reg: 0.2345 (0.2295)  loss_objectness: 0.0831 (0.0793)  loss_rpn_box_reg: 0.0731 (0.0731)  time: 0.2228  data: 0.0068  max mem: 9052\n",
      "Epoch: [3]  [6300/7330]  eta: 0:03:47  lr: 0.000005  loss: 0.6328 (0.6388)  loss_classifier: 0.2557 (0.2569)  loss_box_reg: 0.2252 (0.2295)  loss_objectness: 0.0778 (0.0793)  loss_rpn_box_reg: 0.0767 (0.0732)  time: 0.2215  data: 0.0063  max mem: 9052\n",
      "Epoch: [3]  [6400/7330]  eta: 0:03:25  lr: 0.000005  loss: 0.6000 (0.6388)  loss_classifier: 0.2413 (0.2568)  loss_box_reg: 0.2313 (0.2295)  loss_objectness: 0.0758 (0.0793)  loss_rpn_box_reg: 0.0644 (0.0732)  time: 0.2207  data: 0.0064  max mem: 9052\n",
      "Epoch: [3]  [6500/7330]  eta: 0:03:03  lr: 0.000005  loss: 0.6050 (0.6387)  loss_classifier: 0.2473 (0.2568)  loss_box_reg: 0.2112 (0.2294)  loss_objectness: 0.0713 (0.0793)  loss_rpn_box_reg: 0.0698 (0.0732)  time: 0.2262  data: 0.0065  max mem: 9052\n",
      "Epoch: [3]  [6600/7330]  eta: 0:02:41  lr: 0.000005  loss: 0.5960 (0.6384)  loss_classifier: 0.2449 (0.2567)  loss_box_reg: 0.2175 (0.2293)  loss_objectness: 0.0765 (0.0792)  loss_rpn_box_reg: 0.0674 (0.0731)  time: 0.2155  data: 0.0063  max mem: 9052\n",
      "Epoch: [3]  [6700/7330]  eta: 0:02:19  lr: 0.000005  loss: 0.5979 (0.6382)  loss_classifier: 0.2490 (0.2566)  loss_box_reg: 0.2186 (0.2293)  loss_objectness: 0.0770 (0.0792)  loss_rpn_box_reg: 0.0716 (0.0731)  time: 0.2197  data: 0.0065  max mem: 9052\n",
      "Epoch: [3]  [6800/7330]  eta: 0:01:57  lr: 0.000005  loss: 0.6434 (0.6377)  loss_classifier: 0.2494 (0.2564)  loss_box_reg: 0.2244 (0.2291)  loss_objectness: 0.0816 (0.0792)  loss_rpn_box_reg: 0.0771 (0.0730)  time: 0.2248  data: 0.0063  max mem: 9052\n",
      "Epoch: [3]  [6900/7330]  eta: 0:01:34  lr: 0.000005  loss: 0.6283 (0.6373)  loss_classifier: 0.2557 (0.2563)  loss_box_reg: 0.2199 (0.2289)  loss_objectness: 0.0767 (0.0792)  loss_rpn_box_reg: 0.0739 (0.0729)  time: 0.2177  data: 0.0063  max mem: 9052\n",
      "Epoch: [3]  [7000/7330]  eta: 0:01:12  lr: 0.000005  loss: 0.5971 (0.6373)  loss_classifier: 0.2355 (0.2562)  loss_box_reg: 0.2128 (0.2290)  loss_objectness: 0.0715 (0.0792)  loss_rpn_box_reg: 0.0674 (0.0729)  time: 0.2184  data: 0.0063  max mem: 9052\n",
      "Epoch: [3]  [7100/7330]  eta: 0:00:50  lr: 0.000005  loss: 0.6309 (0.6373)  loss_classifier: 0.2367 (0.2562)  loss_box_reg: 0.2233 (0.2289)  loss_objectness: 0.0805 (0.0792)  loss_rpn_box_reg: 0.0830 (0.0729)  time: 0.2216  data: 0.0061  max mem: 9052\n",
      "Epoch: [3]  [7200/7330]  eta: 0:00:28  lr: 0.000005  loss: 0.6706 (0.6372)  loss_classifier: 0.2668 (0.2561)  loss_box_reg: 0.2396 (0.2289)  loss_objectness: 0.0817 (0.0792)  loss_rpn_box_reg: 0.0700 (0.0729)  time: 0.2162  data: 0.0064  max mem: 9052\n",
      "Epoch: [3]  [7300/7330]  eta: 0:00:06  lr: 0.000005  loss: 0.5844 (0.6370)  loss_classifier: 0.2314 (0.2561)  loss_box_reg: 0.2217 (0.2289)  loss_objectness: 0.0624 (0.0792)  loss_rpn_box_reg: 0.0653 (0.0729)  time: 0.2205  data: 0.0062  max mem: 9052\n",
      "Epoch: [3]  [7329/7330]  eta: 0:00:00  lr: 0.000005  loss: 0.5674 (0.6369)  loss_classifier: 0.2432 (0.2560)  loss_box_reg: 0.2023 (0.2288)  loss_objectness: 0.0696 (0.0792)  loss_rpn_box_reg: 0.0603 (0.0729)  time: 0.2107  data: 0.0061  max mem: 9052\n",
      "Epoch: [3] Total time: 0:26:57 (0.2207 s / it)\n",
      "Test:  [  0/313]  eta: 0:02:25  model_time: 0.1383 (0.1383)  evaluator_time: 0.0275 (0.0275)  time: 0.4657  data: 0.2906  max mem: 9052\n",
      "Test:  [312/313]  eta: 0:00:00  model_time: 0.1299 (0.1307)  evaluator_time: 0.0199 (0.0270)  time: 0.1616  data: 0.0068  max mem: 9052\n",
      "Test: Total time: 0:00:54 (0.1725 s / it)\n",
      "Averaged stats: model_time: 0.1299 (0.1307)  evaluator_time: 0.0199 (0.0270)\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "IoU metric: bbox\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.373\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.610\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.383\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.120\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.431\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.570\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.308\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.363\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.379\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.134\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.435\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.570\n",
      "Epoch: [4]  [   0/7330]  eta: 1:38:35  lr: 0.000005  loss: 0.5273 (0.5273)  loss_classifier: 0.2099 (0.2099)  loss_box_reg: 0.2085 (0.2085)  loss_objectness: 0.0511 (0.0511)  loss_rpn_box_reg: 0.0579 (0.0579)  time: 0.8070  data: 0.5821  max mem: 9052\n",
      "Epoch: [4]  [ 100/7330]  eta: 0:27:17  lr: 0.000005  loss: 0.6349 (0.6232)  loss_classifier: 0.2566 (0.2499)  loss_box_reg: 0.2352 (0.2236)  loss_objectness: 0.0786 (0.0773)  loss_rpn_box_reg: 0.0681 (0.0724)  time: 0.2222  data: 0.0070  max mem: 9052\n",
      "Epoch: [4]  [ 200/7330]  eta: 0:26:42  lr: 0.000005  loss: 0.5831 (0.6262)  loss_classifier: 0.2313 (0.2518)  loss_box_reg: 0.2134 (0.2238)  loss_objectness: 0.0732 (0.0777)  loss_rpn_box_reg: 0.0616 (0.0729)  time: 0.2237  data: 0.0067  max mem: 9052\n",
      "Epoch: [4]  [ 300/7330]  eta: 0:26:10  lr: 0.000005  loss: 0.6197 (0.6276)  loss_classifier: 0.2484 (0.2522)  loss_box_reg: 0.2183 (0.2242)  loss_objectness: 0.0820 (0.0787)  loss_rpn_box_reg: 0.0713 (0.0726)  time: 0.2191  data: 0.0065  max mem: 9052\n",
      "Epoch: [4]  [ 400/7330]  eta: 0:25:40  lr: 0.000005  loss: 0.5784 (0.6290)  loss_classifier: 0.2268 (0.2525)  loss_box_reg: 0.2043 (0.2249)  loss_objectness: 0.0664 (0.0784)  loss_rpn_box_reg: 0.0657 (0.0731)  time: 0.2169  data: 0.0065  max mem: 9052\n",
      "Epoch: [4]  [ 500/7330]  eta: 0:25:13  lr: 0.000005  loss: 0.5918 (0.6278)  loss_classifier: 0.2314 (0.2518)  loss_box_reg: 0.2257 (0.2255)  loss_objectness: 0.0766 (0.0779)  loss_rpn_box_reg: 0.0732 (0.0726)  time: 0.2169  data: 0.0066  max mem: 9052\n",
      "Epoch: [4]  [ 600/7330]  eta: 0:24:50  lr: 0.000005  loss: 0.6127 (0.6247)  loss_classifier: 0.2406 (0.2503)  loss_box_reg: 0.2178 (0.2246)  loss_objectness: 0.0746 (0.0777)  loss_rpn_box_reg: 0.0689 (0.0721)  time: 0.2173  data: 0.0067  max mem: 9052\n",
      "Epoch: [4]  [ 700/7330]  eta: 0:24:25  lr: 0.000005  loss: 0.6057 (0.6235)  loss_classifier: 0.2545 (0.2503)  loss_box_reg: 0.2125 (0.2247)  loss_objectness: 0.0758 (0.0771)  loss_rpn_box_reg: 0.0645 (0.0714)  time: 0.2134  data: 0.0065  max mem: 9052\n",
      "Epoch: [4]  [ 800/7330]  eta: 0:24:02  lr: 0.000005  loss: 0.6513 (0.6247)  loss_classifier: 0.2664 (0.2507)  loss_box_reg: 0.2448 (0.2251)  loss_objectness: 0.0727 (0.0771)  loss_rpn_box_reg: 0.0726 (0.0718)  time: 0.2218  data: 0.0063  max mem: 9052\n",
      "Epoch: [4]  [ 900/7330]  eta: 0:23:39  lr: 0.000005  loss: 0.6218 (0.6246)  loss_classifier: 0.2524 (0.2506)  loss_box_reg: 0.2326 (0.2250)  loss_objectness: 0.0693 (0.0771)  loss_rpn_box_reg: 0.0655 (0.0719)  time: 0.2186  data: 0.0063  max mem: 9052\n",
      "Epoch: [4]  [1000/7330]  eta: 0:23:17  lr: 0.000005  loss: 0.6440 (0.6256)  loss_classifier: 0.2612 (0.2512)  loss_box_reg: 0.2225 (0.2253)  loss_objectness: 0.0724 (0.0770)  loss_rpn_box_reg: 0.0624 (0.0721)  time: 0.2237  data: 0.0063  max mem: 9052\n",
      "Epoch: [4]  [1100/7330]  eta: 0:22:54  lr: 0.000005  loss: 0.5859 (0.6249)  loss_classifier: 0.2331 (0.2508)  loss_box_reg: 0.2121 (0.2250)  loss_objectness: 0.0764 (0.0769)  loss_rpn_box_reg: 0.0668 (0.0720)  time: 0.2158  data: 0.0063  max mem: 9052\n",
      "Epoch: [4]  [1200/7330]  eta: 0:22:32  lr: 0.000005  loss: 0.6304 (0.6245)  loss_classifier: 0.2477 (0.2506)  loss_box_reg: 0.2281 (0.2248)  loss_objectness: 0.0740 (0.0771)  loss_rpn_box_reg: 0.0712 (0.0720)  time: 0.2219  data: 0.0063  max mem: 9052\n",
      "Epoch: [4]  [1300/7330]  eta: 0:22:09  lr: 0.000004  loss: 0.5873 (0.6242)  loss_classifier: 0.2379 (0.2505)  loss_box_reg: 0.2140 (0.2248)  loss_objectness: 0.0702 (0.0771)  loss_rpn_box_reg: 0.0730 (0.0719)  time: 0.2210  data: 0.0063  max mem: 9052\n",
      "Epoch: [4]  [1400/7330]  eta: 0:21:47  lr: 0.000004  loss: 0.6042 (0.6254)  loss_classifier: 0.2375 (0.2510)  loss_box_reg: 0.2128 (0.2250)  loss_objectness: 0.0694 (0.0771)  loss_rpn_box_reg: 0.0719 (0.0723)  time: 0.2206  data: 0.0062  max mem: 9052\n",
      "Epoch: [4]  [1500/7330]  eta: 0:21:25  lr: 0.000004  loss: 0.6401 (0.6264)  loss_classifier: 0.2584 (0.2514)  loss_box_reg: 0.2303 (0.2254)  loss_objectness: 0.0743 (0.0772)  loss_rpn_box_reg: 0.0717 (0.0724)  time: 0.2191  data: 0.0066  max mem: 9052\n",
      "Epoch: [4]  [1600/7330]  eta: 0:21:03  lr: 0.000004  loss: 0.6487 (0.6269)  loss_classifier: 0.2518 (0.2517)  loss_box_reg: 0.2448 (0.2257)  loss_objectness: 0.0749 (0.0774)  loss_rpn_box_reg: 0.0567 (0.0722)  time: 0.2197  data: 0.0062  max mem: 9052\n",
      "Epoch: [4]  [1700/7330]  eta: 0:20:40  lr: 0.000004  loss: 0.6274 (0.6275)  loss_classifier: 0.2523 (0.2520)  loss_box_reg: 0.2326 (0.2258)  loss_objectness: 0.0701 (0.0773)  loss_rpn_box_reg: 0.0700 (0.0723)  time: 0.2148  data: 0.0063  max mem: 9052\n",
      "Epoch: [4]  [1800/7330]  eta: 0:20:18  lr: 0.000004  loss: 0.6281 (0.6276)  loss_classifier: 0.2472 (0.2519)  loss_box_reg: 0.2314 (0.2260)  loss_objectness: 0.0738 (0.0773)  loss_rpn_box_reg: 0.0736 (0.0724)  time: 0.2205  data: 0.0064  max mem: 9052\n",
      "Epoch: [4]  [1900/7330]  eta: 0:19:56  lr: 0.000004  loss: 0.6470 (0.6288)  loss_classifier: 0.2467 (0.2524)  loss_box_reg: 0.2444 (0.2265)  loss_objectness: 0.0822 (0.0775)  loss_rpn_box_reg: 0.0602 (0.0723)  time: 0.2178  data: 0.0060  max mem: 9052\n",
      "Epoch: [4]  [2000/7330]  eta: 0:19:33  lr: 0.000004  loss: 0.6554 (0.6293)  loss_classifier: 0.2653 (0.2528)  loss_box_reg: 0.2248 (0.2266)  loss_objectness: 0.0768 (0.0775)  loss_rpn_box_reg: 0.0715 (0.0724)  time: 0.2167  data: 0.0063  max mem: 9052\n",
      "Epoch: [4]  [2100/7330]  eta: 0:19:12  lr: 0.000004  loss: 0.6221 (0.6297)  loss_classifier: 0.2477 (0.2529)  loss_box_reg: 0.2326 (0.2267)  loss_objectness: 0.0825 (0.0775)  loss_rpn_box_reg: 0.0667 (0.0726)  time: 0.2285  data: 0.0064  max mem: 9052\n",
      "Epoch: [4]  [2200/7330]  eta: 0:18:50  lr: 0.000004  loss: 0.6060 (0.6294)  loss_classifier: 0.2462 (0.2529)  loss_box_reg: 0.2356 (0.2267)  loss_objectness: 0.0691 (0.0774)  loss_rpn_box_reg: 0.0636 (0.0724)  time: 0.2194  data: 0.0068  max mem: 9052\n",
      "Epoch: [4]  [2300/7330]  eta: 0:18:28  lr: 0.000004  loss: 0.6075 (0.6289)  loss_classifier: 0.2545 (0.2527)  loss_box_reg: 0.2152 (0.2265)  loss_objectness: 0.0786 (0.0773)  loss_rpn_box_reg: 0.0651 (0.0724)  time: 0.2189  data: 0.0064  max mem: 9052\n",
      "Epoch: [4]  [2400/7330]  eta: 0:18:07  lr: 0.000004  loss: 0.5632 (0.6279)  loss_classifier: 0.2266 (0.2522)  loss_box_reg: 0.2023 (0.2262)  loss_objectness: 0.0663 (0.0772)  loss_rpn_box_reg: 0.0654 (0.0723)  time: 0.2229  data: 0.0067  max mem: 9052\n",
      "Epoch: [4]  [2500/7330]  eta: 0:17:45  lr: 0.000004  loss: 0.6587 (0.6279)  loss_classifier: 0.2668 (0.2523)  loss_box_reg: 0.2346 (0.2263)  loss_objectness: 0.0738 (0.0772)  loss_rpn_box_reg: 0.0655 (0.0722)  time: 0.2212  data: 0.0066  max mem: 9052\n",
      "Epoch: [4]  [2600/7330]  eta: 0:17:23  lr: 0.000004  loss: 0.6117 (0.6282)  loss_classifier: 0.2570 (0.2524)  loss_box_reg: 0.2217 (0.2264)  loss_objectness: 0.0823 (0.0772)  loss_rpn_box_reg: 0.0764 (0.0722)  time: 0.2168  data: 0.0064  max mem: 9052\n",
      "Epoch: [4]  [2700/7330]  eta: 0:17:01  lr: 0.000004  loss: 0.6722 (0.6285)  loss_classifier: 0.2593 (0.2525)  loss_box_reg: 0.2350 (0.2265)  loss_objectness: 0.0865 (0.0773)  loss_rpn_box_reg: 0.0783 (0.0722)  time: 0.2210  data: 0.0066  max mem: 9052\n",
      "Epoch: [4]  [2800/7330]  eta: 0:16:39  lr: 0.000004  loss: 0.6292 (0.6281)  loss_classifier: 0.2495 (0.2523)  loss_box_reg: 0.2248 (0.2264)  loss_objectness: 0.0767 (0.0773)  loss_rpn_box_reg: 0.0650 (0.0721)  time: 0.2290  data: 0.0066  max mem: 9052\n",
      "Epoch: [4]  [2900/7330]  eta: 0:16:17  lr: 0.000004  loss: 0.5912 (0.6275)  loss_classifier: 0.2353 (0.2521)  loss_box_reg: 0.2138 (0.2263)  loss_objectness: 0.0685 (0.0772)  loss_rpn_box_reg: 0.0696 (0.0720)  time: 0.2246  data: 0.0066  max mem: 9052\n",
      "Epoch: [4]  [3000/7330]  eta: 0:15:55  lr: 0.000004  loss: 0.5828 (0.6268)  loss_classifier: 0.2194 (0.2518)  loss_box_reg: 0.1965 (0.2260)  loss_objectness: 0.0717 (0.0771)  loss_rpn_box_reg: 0.0667 (0.0719)  time: 0.2146  data: 0.0068  max mem: 9052\n",
      "Epoch: [4]  [3100/7330]  eta: 0:15:33  lr: 0.000004  loss: 0.6321 (0.6270)  loss_classifier: 0.2491 (0.2518)  loss_box_reg: 0.2154 (0.2261)  loss_objectness: 0.0754 (0.0772)  loss_rpn_box_reg: 0.0689 (0.0720)  time: 0.2167  data: 0.0065  max mem: 9052\n",
      "Epoch: [4]  [3200/7330]  eta: 0:15:11  lr: 0.000004  loss: 0.6056 (0.6268)  loss_classifier: 0.2401 (0.2517)  loss_box_reg: 0.2174 (0.2261)  loss_objectness: 0.0700 (0.0771)  loss_rpn_box_reg: 0.0690 (0.0720)  time: 0.2182  data: 0.0064  max mem: 9052\n",
      "Epoch: [4]  [3300/7330]  eta: 0:14:49  lr: 0.000004  loss: 0.5981 (0.6268)  loss_classifier: 0.2369 (0.2517)  loss_box_reg: 0.2218 (0.2261)  loss_objectness: 0.0713 (0.0770)  loss_rpn_box_reg: 0.0584 (0.0720)  time: 0.2168  data: 0.0067  max mem: 9052\n",
      "Epoch: [4]  [3400/7330]  eta: 0:14:27  lr: 0.000004  loss: 0.6753 (0.6268)  loss_classifier: 0.2521 (0.2517)  loss_box_reg: 0.2412 (0.2261)  loss_objectness: 0.0808 (0.0770)  loss_rpn_box_reg: 0.0706 (0.0720)  time: 0.2213  data: 0.0066  max mem: 9052\n",
      "Epoch: [4]  [3500/7330]  eta: 0:14:05  lr: 0.000004  loss: 0.5952 (0.6272)  loss_classifier: 0.2483 (0.2518)  loss_box_reg: 0.2206 (0.2263)  loss_objectness: 0.0747 (0.0770)  loss_rpn_box_reg: 0.0696 (0.0721)  time: 0.2233  data: 0.0067  max mem: 9052\n",
      "Epoch: [4]  [3600/7330]  eta: 0:13:43  lr: 0.000004  loss: 0.6522 (0.6274)  loss_classifier: 0.2675 (0.2519)  loss_box_reg: 0.2410 (0.2264)  loss_objectness: 0.0772 (0.0771)  loss_rpn_box_reg: 0.0714 (0.0721)  time: 0.2193  data: 0.0063  max mem: 9052\n",
      "Epoch: [4]  [3700/7330]  eta: 0:13:21  lr: 0.000004  loss: 0.6261 (0.6273)  loss_classifier: 0.2402 (0.2519)  loss_box_reg: 0.2147 (0.2264)  loss_objectness: 0.0759 (0.0771)  loss_rpn_box_reg: 0.0695 (0.0720)  time: 0.2212  data: 0.0061  max mem: 9052\n",
      "Epoch: [4]  [3800/7330]  eta: 0:12:59  lr: 0.000004  loss: 0.6646 (0.6272)  loss_classifier: 0.2723 (0.2519)  loss_box_reg: 0.2370 (0.2264)  loss_objectness: 0.0749 (0.0771)  loss_rpn_box_reg: 0.0731 (0.0719)  time: 0.2197  data: 0.0065  max mem: 9052\n",
      "Epoch: [4]  [3900/7330]  eta: 0:12:37  lr: 0.000004  loss: 0.6436 (0.6273)  loss_classifier: 0.2492 (0.2519)  loss_box_reg: 0.2400 (0.2265)  loss_objectness: 0.0764 (0.0770)  loss_rpn_box_reg: 0.0783 (0.0719)  time: 0.2157  data: 0.0062  max mem: 9052\n",
      "Epoch: [4]  [4000/7330]  eta: 0:12:15  lr: 0.000004  loss: 0.6250 (0.6268)  loss_classifier: 0.2568 (0.2517)  loss_box_reg: 0.2152 (0.2263)  loss_objectness: 0.0705 (0.0770)  loss_rpn_box_reg: 0.0616 (0.0718)  time: 0.2264  data: 0.0068  max mem: 9052\n",
      "Epoch: [4]  [4100/7330]  eta: 0:11:53  lr: 0.000004  loss: 0.6485 (0.6267)  loss_classifier: 0.2498 (0.2517)  loss_box_reg: 0.2389 (0.2263)  loss_objectness: 0.0802 (0.0770)  loss_rpn_box_reg: 0.0685 (0.0718)  time: 0.2292  data: 0.0063  max mem: 9052\n",
      "Epoch: [4]  [4200/7330]  eta: 0:11:31  lr: 0.000004  loss: 0.6210 (0.6262)  loss_classifier: 0.2451 (0.2515)  loss_box_reg: 0.2238 (0.2260)  loss_objectness: 0.0758 (0.0769)  loss_rpn_box_reg: 0.0763 (0.0718)  time: 0.2187  data: 0.0064  max mem: 9052\n",
      "Epoch: [4]  [4300/7330]  eta: 0:11:09  lr: 0.000004  loss: 0.5923 (0.6262)  loss_classifier: 0.2439 (0.2515)  loss_box_reg: 0.2131 (0.2260)  loss_objectness: 0.0771 (0.0770)  loss_rpn_box_reg: 0.0674 (0.0718)  time: 0.2217  data: 0.0068  max mem: 9052\n",
      "Epoch: [4]  [4400/7330]  eta: 0:10:47  lr: 0.000004  loss: 0.6158 (0.6262)  loss_classifier: 0.2561 (0.2515)  loss_box_reg: 0.2326 (0.2261)  loss_objectness: 0.0686 (0.0770)  loss_rpn_box_reg: 0.0641 (0.0717)  time: 0.2203  data: 0.0061  max mem: 9052\n",
      "Epoch: [4]  [4500/7330]  eta: 0:10:24  lr: 0.000004  loss: 0.6485 (0.6264)  loss_classifier: 0.2630 (0.2515)  loss_box_reg: 0.2379 (0.2261)  loss_objectness: 0.0827 (0.0771)  loss_rpn_box_reg: 0.0701 (0.0717)  time: 0.2218  data: 0.0061  max mem: 9052\n",
      "Epoch: [4]  [4600/7330]  eta: 0:10:03  lr: 0.000004  loss: 0.5897 (0.6261)  loss_classifier: 0.2415 (0.2515)  loss_box_reg: 0.2294 (0.2260)  loss_objectness: 0.0710 (0.0770)  loss_rpn_box_reg: 0.0721 (0.0717)  time: 0.2275  data: 0.0063  max mem: 9052\n",
      "Epoch: [4]  [4700/7330]  eta: 0:09:41  lr: 0.000004  loss: 0.6178 (0.6261)  loss_classifier: 0.2384 (0.2514)  loss_box_reg: 0.2172 (0.2260)  loss_objectness: 0.0752 (0.0770)  loss_rpn_box_reg: 0.0781 (0.0717)  time: 0.2179  data: 0.0063  max mem: 9052\n",
      "Epoch: [4]  [4800/7330]  eta: 0:09:18  lr: 0.000004  loss: 0.6181 (0.6261)  loss_classifier: 0.2491 (0.2513)  loss_box_reg: 0.2179 (0.2261)  loss_objectness: 0.0790 (0.0770)  loss_rpn_box_reg: 0.0697 (0.0717)  time: 0.2188  data: 0.0066  max mem: 9052\n",
      "Epoch: [4]  [4900/7330]  eta: 0:08:56  lr: 0.000004  loss: 0.6172 (0.6260)  loss_classifier: 0.2385 (0.2514)  loss_box_reg: 0.2088 (0.2260)  loss_objectness: 0.0741 (0.0770)  loss_rpn_box_reg: 0.0600 (0.0716)  time: 0.2171  data: 0.0069  max mem: 9052\n",
      "Epoch: [4]  [5000/7330]  eta: 0:08:34  lr: 0.000004  loss: 0.6057 (0.6260)  loss_classifier: 0.2385 (0.2514)  loss_box_reg: 0.2213 (0.2261)  loss_objectness: 0.0714 (0.0770)  loss_rpn_box_reg: 0.0646 (0.0716)  time: 0.2244  data: 0.0064  max mem: 9052\n",
      "Epoch: [4]  [5100/7330]  eta: 0:08:12  lr: 0.000003  loss: 0.6006 (0.6259)  loss_classifier: 0.2347 (0.2513)  loss_box_reg: 0.2194 (0.2261)  loss_objectness: 0.0832 (0.0770)  loss_rpn_box_reg: 0.0661 (0.0715)  time: 0.2197  data: 0.0062  max mem: 9052\n",
      "Epoch: [4]  [5200/7330]  eta: 0:07:50  lr: 0.000003  loss: 0.5693 (0.6259)  loss_classifier: 0.2270 (0.2513)  loss_box_reg: 0.2062 (0.2260)  loss_objectness: 0.0703 (0.0770)  loss_rpn_box_reg: 0.0685 (0.0716)  time: 0.2200  data: 0.0061  max mem: 9052\n",
      "Epoch: [4]  [5300/7330]  eta: 0:07:28  lr: 0.000003  loss: 0.6342 (0.6257)  loss_classifier: 0.2496 (0.2512)  loss_box_reg: 0.2179 (0.2259)  loss_objectness: 0.0791 (0.0770)  loss_rpn_box_reg: 0.0677 (0.0716)  time: 0.2200  data: 0.0060  max mem: 9052\n",
      "Epoch: [4]  [5400/7330]  eta: 0:07:06  lr: 0.000003  loss: 0.5621 (0.6257)  loss_classifier: 0.2338 (0.2512)  loss_box_reg: 0.2016 (0.2260)  loss_objectness: 0.0715 (0.0770)  loss_rpn_box_reg: 0.0646 (0.0715)  time: 0.2166  data: 0.0060  max mem: 9052\n",
      "Epoch: [4]  [5500/7330]  eta: 0:06:44  lr: 0.000003  loss: 0.6274 (0.6256)  loss_classifier: 0.2484 (0.2511)  loss_box_reg: 0.2190 (0.2259)  loss_objectness: 0.0715 (0.0770)  loss_rpn_box_reg: 0.0630 (0.0716)  time: 0.2204  data: 0.0060  max mem: 9052\n",
      "Epoch: [4]  [5600/7330]  eta: 0:06:21  lr: 0.000003  loss: 0.6261 (0.6257)  loss_classifier: 0.2478 (0.2511)  loss_box_reg: 0.2149 (0.2260)  loss_objectness: 0.0732 (0.0770)  loss_rpn_box_reg: 0.0733 (0.0716)  time: 0.2233  data: 0.0060  max mem: 9052\n",
      "Epoch: [4]  [5700/7330]  eta: 0:05:59  lr: 0.000003  loss: 0.6000 (0.6257)  loss_classifier: 0.2301 (0.2511)  loss_box_reg: 0.2045 (0.2260)  loss_objectness: 0.0741 (0.0770)  loss_rpn_box_reg: 0.0769 (0.0717)  time: 0.2249  data: 0.0063  max mem: 9052\n",
      "Epoch: [4]  [5800/7330]  eta: 0:05:37  lr: 0.000003  loss: 0.5895 (0.6258)  loss_classifier: 0.2461 (0.2511)  loss_box_reg: 0.2137 (0.2259)  loss_objectness: 0.0800 (0.0770)  loss_rpn_box_reg: 0.0722 (0.0717)  time: 0.2209  data: 0.0064  max mem: 9052\n",
      "Epoch: [4]  [5900/7330]  eta: 0:05:15  lr: 0.000003  loss: 0.6203 (0.6258)  loss_classifier: 0.2442 (0.2511)  loss_box_reg: 0.2112 (0.2259)  loss_objectness: 0.0773 (0.0770)  loss_rpn_box_reg: 0.0740 (0.0717)  time: 0.2188  data: 0.0061  max mem: 9052\n",
      "Epoch: [4]  [6000/7330]  eta: 0:04:53  lr: 0.000003  loss: 0.6174 (0.6254)  loss_classifier: 0.2454 (0.2510)  loss_box_reg: 0.2208 (0.2258)  loss_objectness: 0.0713 (0.0769)  loss_rpn_box_reg: 0.0638 (0.0717)  time: 0.2201  data: 0.0060  max mem: 9052\n",
      "Epoch: [4]  [6100/7330]  eta: 0:04:31  lr: 0.000003  loss: 0.5771 (0.6251)  loss_classifier: 0.2272 (0.2509)  loss_box_reg: 0.2060 (0.2257)  loss_objectness: 0.0712 (0.0769)  loss_rpn_box_reg: 0.0590 (0.0716)  time: 0.2176  data: 0.0062  max mem: 9052\n",
      "Epoch: [4]  [6200/7330]  eta: 0:04:09  lr: 0.000003  loss: 0.6030 (0.6248)  loss_classifier: 0.2500 (0.2508)  loss_box_reg: 0.2169 (0.2256)  loss_objectness: 0.0704 (0.0769)  loss_rpn_box_reg: 0.0621 (0.0715)  time: 0.2203  data: 0.0061  max mem: 9052\n",
      "Epoch: [4]  [6300/7330]  eta: 0:03:47  lr: 0.000003  loss: 0.5815 (0.6250)  loss_classifier: 0.2322 (0.2508)  loss_box_reg: 0.2068 (0.2256)  loss_objectness: 0.0742 (0.0769)  loss_rpn_box_reg: 0.0655 (0.0716)  time: 0.2176  data: 0.0063  max mem: 9052\n",
      "Epoch: [4]  [6400/7330]  eta: 0:03:25  lr: 0.000003  loss: 0.6281 (0.6252)  loss_classifier: 0.2587 (0.2509)  loss_box_reg: 0.2156 (0.2257)  loss_objectness: 0.0771 (0.0770)  loss_rpn_box_reg: 0.0755 (0.0716)  time: 0.2201  data: 0.0061  max mem: 9052\n",
      "Epoch: [4]  [6500/7330]  eta: 0:03:03  lr: 0.000003  loss: 0.6295 (0.6251)  loss_classifier: 0.2582 (0.2509)  loss_box_reg: 0.2296 (0.2257)  loss_objectness: 0.0719 (0.0769)  loss_rpn_box_reg: 0.0587 (0.0716)  time: 0.2166  data: 0.0062  max mem: 9052\n",
      "Epoch: [4]  [6600/7330]  eta: 0:02:41  lr: 0.000003  loss: 0.6047 (0.6250)  loss_classifier: 0.2330 (0.2508)  loss_box_reg: 0.2210 (0.2256)  loss_objectness: 0.0677 (0.0769)  loss_rpn_box_reg: 0.0545 (0.0716)  time: 0.2173  data: 0.0060  max mem: 9052\n",
      "Epoch: [4]  [6700/7330]  eta: 0:02:18  lr: 0.000003  loss: 0.6261 (0.6249)  loss_classifier: 0.2479 (0.2508)  loss_box_reg: 0.2223 (0.2256)  loss_objectness: 0.0716 (0.0769)  loss_rpn_box_reg: 0.0854 (0.0716)  time: 0.2202  data: 0.0067  max mem: 9052\n",
      "Epoch: [4]  [6800/7330]  eta: 0:01:56  lr: 0.000003  loss: 0.5894 (0.6247)  loss_classifier: 0.2360 (0.2507)  loss_box_reg: 0.2049 (0.2255)  loss_objectness: 0.0703 (0.0769)  loss_rpn_box_reg: 0.0726 (0.0716)  time: 0.2216  data: 0.0060  max mem: 9052\n",
      "Epoch: [4]  [6900/7330]  eta: 0:01:34  lr: 0.000003  loss: 0.6029 (0.6246)  loss_classifier: 0.2349 (0.2506)  loss_box_reg: 0.2320 (0.2255)  loss_objectness: 0.0681 (0.0769)  loss_rpn_box_reg: 0.0700 (0.0716)  time: 0.2194  data: 0.0060  max mem: 9052\n",
      "Epoch: [4]  [7000/7330]  eta: 0:01:12  lr: 0.000003  loss: 0.5884 (0.6246)  loss_classifier: 0.2305 (0.2507)  loss_box_reg: 0.2105 (0.2255)  loss_objectness: 0.0670 (0.0768)  loss_rpn_box_reg: 0.0554 (0.0716)  time: 0.2211  data: 0.0063  max mem: 9052\n",
      "Epoch: [4]  [7100/7330]  eta: 0:00:50  lr: 0.000003  loss: 0.5900 (0.6246)  loss_classifier: 0.2371 (0.2506)  loss_box_reg: 0.2050 (0.2255)  loss_objectness: 0.0643 (0.0768)  loss_rpn_box_reg: 0.0691 (0.0716)  time: 0.2251  data: 0.0062  max mem: 9052\n",
      "Epoch: [4]  [7200/7330]  eta: 0:00:28  lr: 0.000003  loss: 0.6419 (0.6246)  loss_classifier: 0.2471 (0.2506)  loss_box_reg: 0.2292 (0.2255)  loss_objectness: 0.0683 (0.0768)  loss_rpn_box_reg: 0.0765 (0.0717)  time: 0.2184  data: 0.0061  max mem: 9052\n",
      "Epoch: [4]  [7300/7330]  eta: 0:00:06  lr: 0.000003  loss: 0.6095 (0.6246)  loss_classifier: 0.2440 (0.2506)  loss_box_reg: 0.2228 (0.2255)  loss_objectness: 0.0678 (0.0768)  loss_rpn_box_reg: 0.0682 (0.0717)  time: 0.2188  data: 0.0060  max mem: 9052\n",
      "Epoch: [4]  [7329/7330]  eta: 0:00:00  lr: 0.000003  loss: 0.5844 (0.6245)  loss_classifier: 0.2399 (0.2505)  loss_box_reg: 0.2168 (0.2255)  loss_objectness: 0.0771 (0.0768)  loss_rpn_box_reg: 0.0643 (0.0717)  time: 0.2149  data: 0.0061  max mem: 9052\n",
      "Epoch: [4] Total time: 0:26:56 (0.2205 s / it)\n",
      "Test:  [  0/313]  eta: 0:02:22  model_time: 0.1374 (0.1374)  evaluator_time: 0.0268 (0.0268)  time: 0.4546  data: 0.2815  max mem: 9052\n",
      "Test:  [312/313]  eta: 0:00:00  model_time: 0.1289 (0.1301)  evaluator_time: 0.0181 (0.0250)  time: 0.1580  data: 0.0063  max mem: 9052\n",
      "Test: Total time: 0:00:52 (0.1689 s / it)\n",
      "Averaged stats: model_time: 0.1289 (0.1301)  evaluator_time: 0.0181 (0.0250)\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "IoU metric: bbox\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.335\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.619\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.285\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.124\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.378\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.527\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.262\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.330\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.345\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.134\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.394\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.527\n",
      "Epoch: [5]  [   0/7330]  eta: 1:26:54  lr: 0.000003  loss: 0.5705 (0.5705)  loss_classifier: 0.2446 (0.2446)  loss_box_reg: 0.2200 (0.2200)  loss_objectness: 0.0610 (0.0610)  loss_rpn_box_reg: 0.0449 (0.0449)  time: 0.7113  data: 0.4731  max mem: 9052\n",
      "Epoch: [5]  [ 100/7330]  eta: 0:27:13  lr: 0.000003  loss: 0.5729 (0.5967)  loss_classifier: 0.2204 (0.2379)  loss_box_reg: 0.1979 (0.2161)  loss_objectness: 0.0695 (0.0729)  loss_rpn_box_reg: 0.0665 (0.0698)  time: 0.2187  data: 0.0067  max mem: 9052\n",
      "Epoch: [5]  [ 200/7330]  eta: 0:26:33  lr: 0.000003  loss: 0.5917 (0.6039)  loss_classifier: 0.2431 (0.2423)  loss_box_reg: 0.2099 (0.2193)  loss_objectness: 0.0743 (0.0730)  loss_rpn_box_reg: 0.0571 (0.0693)  time: 0.2223  data: 0.0065  max mem: 9052\n",
      "Epoch: [5]  [ 300/7330]  eta: 0:26:04  lr: 0.000003  loss: 0.5883 (0.6061)  loss_classifier: 0.2256 (0.2427)  loss_box_reg: 0.2073 (0.2200)  loss_objectness: 0.0749 (0.0740)  loss_rpn_box_reg: 0.0659 (0.0694)  time: 0.2204  data: 0.0065  max mem: 9052\n",
      "Epoch: [5]  [ 400/7330]  eta: 0:25:34  lr: 0.000003  loss: 0.6119 (0.6083)  loss_classifier: 0.2453 (0.2433)  loss_box_reg: 0.2236 (0.2198)  loss_objectness: 0.0757 (0.0750)  loss_rpn_box_reg: 0.0736 (0.0701)  time: 0.2179  data: 0.0066  max mem: 9052\n",
      "Epoch: [5]  [ 500/7330]  eta: 0:25:11  lr: 0.000003  loss: 0.6528 (0.6141)  loss_classifier: 0.2743 (0.2466)  loss_box_reg: 0.2364 (0.2217)  loss_objectness: 0.0775 (0.0755)  loss_rpn_box_reg: 0.0610 (0.0703)  time: 0.2224  data: 0.0067  max mem: 9052\n",
      "Epoch: [5]  [ 600/7330]  eta: 0:24:53  lr: 0.000003  loss: 0.6415 (0.6153)  loss_classifier: 0.2526 (0.2470)  loss_box_reg: 0.2382 (0.2228)  loss_objectness: 0.0760 (0.0754)  loss_rpn_box_reg: 0.0668 (0.0702)  time: 0.2242  data: 0.0067  max mem: 9052\n",
      "Epoch: [5]  [ 700/7330]  eta: 0:24:31  lr: 0.000003  loss: 0.5931 (0.6156)  loss_classifier: 0.2306 (0.2474)  loss_box_reg: 0.2102 (0.2228)  loss_objectness: 0.0715 (0.0753)  loss_rpn_box_reg: 0.0644 (0.0701)  time: 0.2204  data: 0.0065  max mem: 9052\n",
      "Epoch: [5]  [ 800/7330]  eta: 0:24:07  lr: 0.000003  loss: 0.5766 (0.6171)  loss_classifier: 0.2381 (0.2481)  loss_box_reg: 0.2147 (0.2236)  loss_objectness: 0.0742 (0.0754)  loss_rpn_box_reg: 0.0668 (0.0701)  time: 0.2282  data: 0.0062  max mem: 9052\n",
      "Epoch: [5]  [ 900/7330]  eta: 0:23:43  lr: 0.000003  loss: 0.6336 (0.6178)  loss_classifier: 0.2589 (0.2483)  loss_box_reg: 0.2280 (0.2242)  loss_objectness: 0.0736 (0.0755)  loss_rpn_box_reg: 0.0719 (0.0698)  time: 0.2165  data: 0.0062  max mem: 9052\n",
      "Epoch: [5]  [1000/7330]  eta: 0:23:22  lr: 0.000003  loss: 0.5810 (0.6186)  loss_classifier: 0.2410 (0.2484)  loss_box_reg: 0.2253 (0.2246)  loss_objectness: 0.0721 (0.0758)  loss_rpn_box_reg: 0.0543 (0.0698)  time: 0.2217  data: 0.0064  max mem: 9052\n",
      "Epoch: [5]  [1100/7330]  eta: 0:23:00  lr: 0.000003  loss: 0.6397 (0.6186)  loss_classifier: 0.2489 (0.2482)  loss_box_reg: 0.2302 (0.2246)  loss_objectness: 0.0714 (0.0759)  loss_rpn_box_reg: 0.0737 (0.0699)  time: 0.2261  data: 0.0065  max mem: 9052\n",
      "Epoch: [5]  [1200/7330]  eta: 0:22:38  lr: 0.000003  loss: 0.6032 (0.6172)  loss_classifier: 0.2440 (0.2478)  loss_box_reg: 0.2055 (0.2242)  loss_objectness: 0.0648 (0.0754)  loss_rpn_box_reg: 0.0758 (0.0697)  time: 0.2238  data: 0.0068  max mem: 9052\n",
      "Epoch: [5]  [1300/7330]  eta: 0:22:16  lr: 0.000003  loss: 0.5875 (0.6147)  loss_classifier: 0.2312 (0.2465)  loss_box_reg: 0.2302 (0.2233)  loss_objectness: 0.0639 (0.0752)  loss_rpn_box_reg: 0.0619 (0.0697)  time: 0.2244  data: 0.0079  max mem: 9052\n",
      "Epoch: [5]  [1400/7330]  eta: 0:21:55  lr: 0.000003  loss: 0.5675 (0.6140)  loss_classifier: 0.2312 (0.2463)  loss_box_reg: 0.2206 (0.2228)  loss_objectness: 0.0749 (0.0751)  loss_rpn_box_reg: 0.0674 (0.0698)  time: 0.2216  data: 0.0064  max mem: 9052\n",
      "Epoch: [5]  [1500/7330]  eta: 0:21:33  lr: 0.000003  loss: 0.5700 (0.6139)  loss_classifier: 0.2278 (0.2462)  loss_box_reg: 0.2092 (0.2228)  loss_objectness: 0.0696 (0.0752)  loss_rpn_box_reg: 0.0611 (0.0697)  time: 0.2234  data: 0.0063  max mem: 9052\n",
      "Epoch: [5]  [1600/7330]  eta: 0:21:11  lr: 0.000003  loss: 0.6372 (0.6149)  loss_classifier: 0.2581 (0.2466)  loss_box_reg: 0.2177 (0.2230)  loss_objectness: 0.0746 (0.0754)  loss_rpn_box_reg: 0.0629 (0.0699)  time: 0.2243  data: 0.0064  max mem: 9052\n",
      "Epoch: [5]  [1700/7330]  eta: 0:20:48  lr: 0.000003  loss: 0.6014 (0.6155)  loss_classifier: 0.2538 (0.2469)  loss_box_reg: 0.2123 (0.2231)  loss_objectness: 0.0763 (0.0757)  loss_rpn_box_reg: 0.0645 (0.0699)  time: 0.2210  data: 0.0066  max mem: 9052\n",
      "Epoch: [5]  [1800/7330]  eta: 0:20:26  lr: 0.000003  loss: 0.5643 (0.6150)  loss_classifier: 0.2261 (0.2467)  loss_box_reg: 0.2059 (0.2229)  loss_objectness: 0.0684 (0.0755)  loss_rpn_box_reg: 0.0759 (0.0699)  time: 0.2263  data: 0.0070  max mem: 9052\n",
      "Epoch: [5]  [1900/7330]  eta: 0:20:04  lr: 0.000003  loss: 0.6545 (0.6156)  loss_classifier: 0.2648 (0.2470)  loss_box_reg: 0.2214 (0.2230)  loss_objectness: 0.0788 (0.0756)  loss_rpn_box_reg: 0.0777 (0.0700)  time: 0.2224  data: 0.0065  max mem: 9052\n",
      "Epoch: [5]  [2000/7330]  eta: 0:19:42  lr: 0.000002  loss: 0.5902 (0.6155)  loss_classifier: 0.2406 (0.2470)  loss_box_reg: 0.2035 (0.2229)  loss_objectness: 0.0707 (0.0755)  loss_rpn_box_reg: 0.0671 (0.0701)  time: 0.2178  data: 0.0064  max mem: 9052\n",
      "Epoch: [5]  [2100/7330]  eta: 0:19:20  lr: 0.000002  loss: 0.6100 (0.6150)  loss_classifier: 0.2433 (0.2467)  loss_box_reg: 0.2177 (0.2227)  loss_objectness: 0.0754 (0.0755)  loss_rpn_box_reg: 0.0676 (0.0700)  time: 0.2254  data: 0.0065  max mem: 9052\n",
      "Epoch: [5]  [2200/7330]  eta: 0:18:58  lr: 0.000002  loss: 0.5899 (0.6148)  loss_classifier: 0.2484 (0.2467)  loss_box_reg: 0.2233 (0.2227)  loss_objectness: 0.0673 (0.0755)  loss_rpn_box_reg: 0.0578 (0.0700)  time: 0.2291  data: 0.0067  max mem: 9052\n",
      "Epoch: [5]  [2300/7330]  eta: 0:18:36  lr: 0.000002  loss: 0.5727 (0.6148)  loss_classifier: 0.2233 (0.2467)  loss_box_reg: 0.2092 (0.2228)  loss_objectness: 0.0645 (0.0754)  loss_rpn_box_reg: 0.0677 (0.0700)  time: 0.2240  data: 0.0064  max mem: 9052\n",
      "Epoch: [5]  [2400/7330]  eta: 0:18:13  lr: 0.000002  loss: 0.6021 (0.6149)  loss_classifier: 0.2462 (0.2467)  loss_box_reg: 0.2272 (0.2228)  loss_objectness: 0.0696 (0.0754)  loss_rpn_box_reg: 0.0612 (0.0701)  time: 0.2227  data: 0.0065  max mem: 9052\n",
      "Epoch: [5]  [2500/7330]  eta: 0:17:51  lr: 0.000002  loss: 0.5622 (0.6147)  loss_classifier: 0.2313 (0.2466)  loss_box_reg: 0.2124 (0.2227)  loss_objectness: 0.0707 (0.0754)  loss_rpn_box_reg: 0.0633 (0.0700)  time: 0.2264  data: 0.0063  max mem: 9052\n",
      "Epoch: [5]  [2600/7330]  eta: 0:17:29  lr: 0.000002  loss: 0.5976 (0.6153)  loss_classifier: 0.2462 (0.2468)  loss_box_reg: 0.2235 (0.2230)  loss_objectness: 0.0748 (0.0754)  loss_rpn_box_reg: 0.0699 (0.0701)  time: 0.2198  data: 0.0065  max mem: 9052\n",
      "Epoch: [5]  [2700/7330]  eta: 0:17:07  lr: 0.000002  loss: 0.5806 (0.6152)  loss_classifier: 0.2387 (0.2467)  loss_box_reg: 0.2153 (0.2229)  loss_objectness: 0.0762 (0.0755)  loss_rpn_box_reg: 0.0570 (0.0701)  time: 0.2244  data: 0.0062  max mem: 9052\n",
      "Epoch: [5]  [2800/7330]  eta: 0:16:45  lr: 0.000002  loss: 0.6561 (0.6156)  loss_classifier: 0.2626 (0.2469)  loss_box_reg: 0.2336 (0.2231)  loss_objectness: 0.0771 (0.0755)  loss_rpn_box_reg: 0.0822 (0.0700)  time: 0.2163  data: 0.0064  max mem: 9052\n",
      "Epoch: [5]  [2900/7330]  eta: 0:16:22  lr: 0.000002  loss: 0.6441 (0.6156)  loss_classifier: 0.2598 (0.2469)  loss_box_reg: 0.2242 (0.2231)  loss_objectness: 0.0764 (0.0755)  loss_rpn_box_reg: 0.0776 (0.0701)  time: 0.2161  data: 0.0061  max mem: 9052\n",
      "Epoch: [5]  [3000/7330]  eta: 0:15:59  lr: 0.000002  loss: 0.5802 (0.6156)  loss_classifier: 0.2382 (0.2468)  loss_box_reg: 0.2091 (0.2230)  loss_objectness: 0.0705 (0.0756)  loss_rpn_box_reg: 0.0816 (0.0702)  time: 0.2184  data: 0.0063  max mem: 9052\n",
      "Epoch: [5]  [3100/7330]  eta: 0:15:37  lr: 0.000002  loss: 0.6273 (0.6156)  loss_classifier: 0.2398 (0.2468)  loss_box_reg: 0.2231 (0.2229)  loss_objectness: 0.0760 (0.0756)  loss_rpn_box_reg: 0.0707 (0.0702)  time: 0.2187  data: 0.0064  max mem: 9052\n",
      "Epoch: [5]  [3200/7330]  eta: 0:15:15  lr: 0.000002  loss: 0.6656 (0.6154)  loss_classifier: 0.2631 (0.2467)  loss_box_reg: 0.2409 (0.2229)  loss_objectness: 0.0678 (0.0755)  loss_rpn_box_reg: 0.0675 (0.0703)  time: 0.2253  data: 0.0062  max mem: 9052\n",
      "Epoch: [5]  [3300/7330]  eta: 0:14:52  lr: 0.000002  loss: 0.5923 (0.6156)  loss_classifier: 0.2309 (0.2467)  loss_box_reg: 0.2154 (0.2230)  loss_objectness: 0.0743 (0.0755)  loss_rpn_box_reg: 0.0788 (0.0704)  time: 0.2203  data: 0.0060  max mem: 9052\n",
      "Epoch: [5]  [3400/7330]  eta: 0:14:30  lr: 0.000002  loss: 0.5907 (0.6158)  loss_classifier: 0.2359 (0.2468)  loss_box_reg: 0.2236 (0.2230)  loss_objectness: 0.0711 (0.0756)  loss_rpn_box_reg: 0.0682 (0.0705)  time: 0.2147  data: 0.0061  max mem: 9052\n",
      "Epoch: [5]  [3500/7330]  eta: 0:14:07  lr: 0.000002  loss: 0.6424 (0.6156)  loss_classifier: 0.2610 (0.2467)  loss_box_reg: 0.2276 (0.2229)  loss_objectness: 0.0807 (0.0755)  loss_rpn_box_reg: 0.0659 (0.0705)  time: 0.2205  data: 0.0062  max mem: 9052\n",
      "Epoch: [5]  [3600/7330]  eta: 0:13:45  lr: 0.000002  loss: 0.6597 (0.6156)  loss_classifier: 0.2497 (0.2466)  loss_box_reg: 0.2209 (0.2229)  loss_objectness: 0.0702 (0.0755)  loss_rpn_box_reg: 0.0772 (0.0706)  time: 0.2197  data: 0.0066  max mem: 9052\n",
      "Epoch: [5]  [3700/7330]  eta: 0:13:23  lr: 0.000002  loss: 0.5751 (0.6154)  loss_classifier: 0.2313 (0.2465)  loss_box_reg: 0.2215 (0.2228)  loss_objectness: 0.0773 (0.0755)  loss_rpn_box_reg: 0.0645 (0.0706)  time: 0.2214  data: 0.0062  max mem: 9052\n",
      "Epoch: [5]  [3800/7330]  eta: 0:13:00  lr: 0.000002  loss: 0.5900 (0.6157)  loss_classifier: 0.2282 (0.2466)  loss_box_reg: 0.2088 (0.2228)  loss_objectness: 0.0711 (0.0755)  loss_rpn_box_reg: 0.0743 (0.0707)  time: 0.2144  data: 0.0062  max mem: 9052\n",
      "Epoch: [5]  [3900/7330]  eta: 0:12:38  lr: 0.000002  loss: 0.6012 (0.6156)  loss_classifier: 0.2445 (0.2466)  loss_box_reg: 0.2181 (0.2228)  loss_objectness: 0.0791 (0.0755)  loss_rpn_box_reg: 0.0654 (0.0707)  time: 0.2116  data: 0.0062  max mem: 9052\n",
      "Epoch: [5]  [4000/7330]  eta: 0:12:15  lr: 0.000002  loss: 0.6113 (0.6156)  loss_classifier: 0.2313 (0.2466)  loss_box_reg: 0.2094 (0.2228)  loss_objectness: 0.0726 (0.0755)  loss_rpn_box_reg: 0.0695 (0.0708)  time: 0.2208  data: 0.0060  max mem: 9052\n",
      "Epoch: [5]  [4100/7330]  eta: 0:11:53  lr: 0.000002  loss: 0.5829 (0.6157)  loss_classifier: 0.2358 (0.2466)  loss_box_reg: 0.2115 (0.2227)  loss_objectness: 0.0676 (0.0756)  loss_rpn_box_reg: 0.0570 (0.0708)  time: 0.2216  data: 0.0060  max mem: 9052\n",
      "Epoch: [5]  [4200/7330]  eta: 0:11:31  lr: 0.000002  loss: 0.6119 (0.6159)  loss_classifier: 0.2446 (0.2467)  loss_box_reg: 0.2127 (0.2228)  loss_objectness: 0.0707 (0.0755)  loss_rpn_box_reg: 0.0672 (0.0709)  time: 0.2230  data: 0.0061  max mem: 9052\n",
      "Epoch: [5]  [4300/7330]  eta: 0:11:09  lr: 0.000002  loss: 0.6146 (0.6162)  loss_classifier: 0.2469 (0.2468)  loss_box_reg: 0.2173 (0.2229)  loss_objectness: 0.0729 (0.0755)  loss_rpn_box_reg: 0.0659 (0.0709)  time: 0.2205  data: 0.0064  max mem: 9052\n",
      "Epoch: [5]  [4400/7330]  eta: 0:10:46  lr: 0.000002  loss: 0.6482 (0.6162)  loss_classifier: 0.2656 (0.2468)  loss_box_reg: 0.2220 (0.2229)  loss_objectness: 0.0770 (0.0755)  loss_rpn_box_reg: 0.0734 (0.0710)  time: 0.2165  data: 0.0061  max mem: 9052\n",
      "Epoch: [5]  [4500/7330]  eta: 0:10:24  lr: 0.000002  loss: 0.5805 (0.6162)  loss_classifier: 0.2322 (0.2468)  loss_box_reg: 0.2083 (0.2229)  loss_objectness: 0.0692 (0.0755)  loss_rpn_box_reg: 0.0671 (0.0710)  time: 0.2211  data: 0.0059  max mem: 9052\n",
      "Epoch: [5]  [4600/7330]  eta: 0:10:02  lr: 0.000002  loss: 0.6260 (0.6161)  loss_classifier: 0.2515 (0.2468)  loss_box_reg: 0.2297 (0.2230)  loss_objectness: 0.0653 (0.0754)  loss_rpn_box_reg: 0.0657 (0.0709)  time: 0.2242  data: 0.0062  max mem: 9052\n",
      "Epoch: [5]  [4700/7330]  eta: 0:09:40  lr: 0.000002  loss: 0.6001 (0.6162)  loss_classifier: 0.2276 (0.2468)  loss_box_reg: 0.2052 (0.2230)  loss_objectness: 0.0682 (0.0755)  loss_rpn_box_reg: 0.0641 (0.0710)  time: 0.2164  data: 0.0064  max mem: 9052\n",
      "Epoch: [5]  [4800/7330]  eta: 0:09:18  lr: 0.000002  loss: 0.5635 (0.6161)  loss_classifier: 0.2329 (0.2468)  loss_box_reg: 0.2002 (0.2230)  loss_objectness: 0.0694 (0.0754)  loss_rpn_box_reg: 0.0520 (0.0708)  time: 0.2192  data: 0.0063  max mem: 9052\n",
      "Epoch: [5]  [4900/7330]  eta: 0:08:56  lr: 0.000002  loss: 0.6427 (0.6159)  loss_classifier: 0.2485 (0.2467)  loss_box_reg: 0.2385 (0.2229)  loss_objectness: 0.0703 (0.0754)  loss_rpn_box_reg: 0.0750 (0.0709)  time: 0.2226  data: 0.0063  max mem: 9052\n",
      "Epoch: [5]  [5000/7330]  eta: 0:08:33  lr: 0.000002  loss: 0.5878 (0.6158)  loss_classifier: 0.2370 (0.2467)  loss_box_reg: 0.2184 (0.2229)  loss_objectness: 0.0662 (0.0753)  loss_rpn_box_reg: 0.0736 (0.0709)  time: 0.2174  data: 0.0061  max mem: 9052\n",
      "Epoch: [5]  [5100/7330]  eta: 0:08:11  lr: 0.000002  loss: 0.5980 (0.6157)  loss_classifier: 0.2297 (0.2467)  loss_box_reg: 0.2069 (0.2228)  loss_objectness: 0.0638 (0.0753)  loss_rpn_box_reg: 0.0708 (0.0710)  time: 0.2177  data: 0.0062  max mem: 9052\n",
      "Epoch: [5]  [5200/7330]  eta: 0:07:49  lr: 0.000002  loss: 0.5403 (0.6158)  loss_classifier: 0.2201 (0.2467)  loss_box_reg: 0.2023 (0.2229)  loss_objectness: 0.0692 (0.0753)  loss_rpn_box_reg: 0.0648 (0.0710)  time: 0.2172  data: 0.0060  max mem: 9052\n",
      "Epoch: [5]  [5300/7330]  eta: 0:07:27  lr: 0.000002  loss: 0.5839 (0.6161)  loss_classifier: 0.2445 (0.2468)  loss_box_reg: 0.2229 (0.2229)  loss_objectness: 0.0692 (0.0753)  loss_rpn_box_reg: 0.0585 (0.0710)  time: 0.2148  data: 0.0063  max mem: 9052\n",
      "Epoch: [5]  [5400/7330]  eta: 0:07:05  lr: 0.000002  loss: 0.6034 (0.6162)  loss_classifier: 0.2423 (0.2468)  loss_box_reg: 0.2285 (0.2230)  loss_objectness: 0.0793 (0.0754)  loss_rpn_box_reg: 0.0706 (0.0711)  time: 0.2161  data: 0.0062  max mem: 9052\n",
      "Epoch: [5]  [5500/7330]  eta: 0:06:43  lr: 0.000002  loss: 0.6191 (0.6163)  loss_classifier: 0.2472 (0.2469)  loss_box_reg: 0.2221 (0.2230)  loss_objectness: 0.0714 (0.0754)  loss_rpn_box_reg: 0.0735 (0.0711)  time: 0.2176  data: 0.0062  max mem: 9052\n",
      "Epoch: [5]  [5600/7330]  eta: 0:06:21  lr: 0.000002  loss: 0.6016 (0.6162)  loss_classifier: 0.2235 (0.2468)  loss_box_reg: 0.2149 (0.2229)  loss_objectness: 0.0702 (0.0754)  loss_rpn_box_reg: 0.0642 (0.0711)  time: 0.2157  data: 0.0060  max mem: 9052\n",
      "Epoch: [5]  [5700/7330]  eta: 0:05:59  lr: 0.000002  loss: 0.5926 (0.6160)  loss_classifier: 0.2432 (0.2467)  loss_box_reg: 0.2231 (0.2229)  loss_objectness: 0.0744 (0.0754)  loss_rpn_box_reg: 0.0608 (0.0710)  time: 0.2233  data: 0.0062  max mem: 9052\n",
      "Epoch: [5]  [5800/7330]  eta: 0:05:37  lr: 0.000002  loss: 0.5727 (0.6163)  loss_classifier: 0.2327 (0.2468)  loss_box_reg: 0.2116 (0.2230)  loss_objectness: 0.0675 (0.0754)  loss_rpn_box_reg: 0.0676 (0.0711)  time: 0.2218  data: 0.0060  max mem: 9052\n",
      "Epoch: [5]  [5900/7330]  eta: 0:05:14  lr: 0.000002  loss: 0.6225 (0.6164)  loss_classifier: 0.2375 (0.2469)  loss_box_reg: 0.2253 (0.2230)  loss_objectness: 0.0750 (0.0755)  loss_rpn_box_reg: 0.0655 (0.0710)  time: 0.2196  data: 0.0061  max mem: 9052\n",
      "Epoch: [5]  [6000/7330]  eta: 0:04:52  lr: 0.000002  loss: 0.6381 (0.6166)  loss_classifier: 0.2562 (0.2470)  loss_box_reg: 0.2349 (0.2231)  loss_objectness: 0.0802 (0.0755)  loss_rpn_box_reg: 0.0762 (0.0711)  time: 0.2200  data: 0.0059  max mem: 9052\n",
      "Epoch: [5]  [6100/7330]  eta: 0:04:30  lr: 0.000002  loss: 0.6381 (0.6170)  loss_classifier: 0.2591 (0.2471)  loss_box_reg: 0.2237 (0.2232)  loss_objectness: 0.0770 (0.0756)  loss_rpn_box_reg: 0.0651 (0.0711)  time: 0.2217  data: 0.0060  max mem: 9052\n",
      "Epoch: [5]  [6200/7330]  eta: 0:04:08  lr: 0.000002  loss: 0.6387 (0.6171)  loss_classifier: 0.2575 (0.2472)  loss_box_reg: 0.2286 (0.2232)  loss_objectness: 0.0710 (0.0756)  loss_rpn_box_reg: 0.0675 (0.0711)  time: 0.2229  data: 0.0061  max mem: 9052\n",
      "Epoch: [5]  [6300/7330]  eta: 0:03:46  lr: 0.000002  loss: 0.6129 (0.6171)  loss_classifier: 0.2511 (0.2472)  loss_box_reg: 0.2183 (0.2232)  loss_objectness: 0.0789 (0.0756)  loss_rpn_box_reg: 0.0688 (0.0711)  time: 0.2200  data: 0.0061  max mem: 9052\n",
      "Epoch: [5]  [6400/7330]  eta: 0:03:24  lr: 0.000002  loss: 0.6222 (0.6169)  loss_classifier: 0.2347 (0.2471)  loss_box_reg: 0.2175 (0.2231)  loss_objectness: 0.0770 (0.0756)  loss_rpn_box_reg: 0.0613 (0.0711)  time: 0.2165  data: 0.0062  max mem: 9052\n",
      "Epoch: [5]  [6500/7330]  eta: 0:03:02  lr: 0.000002  loss: 0.6303 (0.6169)  loss_classifier: 0.2554 (0.2471)  loss_box_reg: 0.2275 (0.2232)  loss_objectness: 0.0778 (0.0756)  loss_rpn_box_reg: 0.0625 (0.0710)  time: 0.2142  data: 0.0063  max mem: 9052\n",
      "Epoch: [5]  [6600/7330]  eta: 0:02:40  lr: 0.000002  loss: 0.5883 (0.6170)  loss_classifier: 0.2353 (0.2472)  loss_box_reg: 0.2077 (0.2232)  loss_objectness: 0.0696 (0.0756)  loss_rpn_box_reg: 0.0722 (0.0710)  time: 0.2160  data: 0.0063  max mem: 9052\n",
      "Epoch: [5]  [6700/7330]  eta: 0:02:18  lr: 0.000002  loss: 0.5344 (0.6168)  loss_classifier: 0.2218 (0.2471)  loss_box_reg: 0.1968 (0.2231)  loss_objectness: 0.0621 (0.0756)  loss_rpn_box_reg: 0.0561 (0.0710)  time: 0.2245  data: 0.0066  max mem: 9052\n",
      "Epoch: [5]  [6800/7330]  eta: 0:01:56  lr: 0.000002  loss: 0.5952 (0.6167)  loss_classifier: 0.2334 (0.2471)  loss_box_reg: 0.2203 (0.2230)  loss_objectness: 0.0727 (0.0756)  loss_rpn_box_reg: 0.0709 (0.0710)  time: 0.2184  data: 0.0061  max mem: 9052\n",
      "Epoch: [5]  [6900/7330]  eta: 0:01:34  lr: 0.000002  loss: 0.6056 (0.6166)  loss_classifier: 0.2358 (0.2471)  loss_box_reg: 0.2169 (0.2230)  loss_objectness: 0.0696 (0.0756)  loss_rpn_box_reg: 0.0737 (0.0710)  time: 0.2203  data: 0.0062  max mem: 9052\n",
      "Epoch: [5]  [7000/7330]  eta: 0:01:12  lr: 0.000002  loss: 0.6177 (0.6166)  loss_classifier: 0.2370 (0.2470)  loss_box_reg: 0.2239 (0.2230)  loss_objectness: 0.0712 (0.0755)  loss_rpn_box_reg: 0.0651 (0.0710)  time: 0.2156  data: 0.0063  max mem: 9052\n",
      "Epoch: [5]  [7100/7330]  eta: 0:00:50  lr: 0.000002  loss: 0.6333 (0.6166)  loss_classifier: 0.2615 (0.2470)  loss_box_reg: 0.2308 (0.2229)  loss_objectness: 0.0778 (0.0756)  loss_rpn_box_reg: 0.0642 (0.0711)  time: 0.2161  data: 0.0062  max mem: 9052\n",
      "Epoch: [5]  [7200/7330]  eta: 0:00:28  lr: 0.000002  loss: 0.5904 (0.6165)  loss_classifier: 0.2483 (0.2470)  loss_box_reg: 0.2227 (0.2229)  loss_objectness: 0.0691 (0.0756)  loss_rpn_box_reg: 0.0520 (0.0710)  time: 0.2181  data: 0.0061  max mem: 9052\n",
      "Epoch: [5]  [7300/7330]  eta: 0:00:06  lr: 0.000002  loss: 0.6160 (0.6167)  loss_classifier: 0.2481 (0.2470)  loss_box_reg: 0.2170 (0.2230)  loss_objectness: 0.0766 (0.0756)  loss_rpn_box_reg: 0.0781 (0.0711)  time: 0.2207  data: 0.0060  max mem: 9052\n",
      "Epoch: [5]  [7329/7330]  eta: 0:00:00  lr: 0.000002  loss: 0.5688 (0.6165)  loss_classifier: 0.2260 (0.2470)  loss_box_reg: 0.2118 (0.2229)  loss_objectness: 0.0646 (0.0755)  loss_rpn_box_reg: 0.0602 (0.0711)  time: 0.2101  data: 0.0062  max mem: 9052\n",
      "Epoch: [5] Total time: 0:26:53 (0.2201 s / it)\n",
      "Test:  [  0/313]  eta: 0:02:25  model_time: 0.1376 (0.1376)  evaluator_time: 0.0282 (0.0282)  time: 0.4660  data: 0.2917  max mem: 9052\n",
      "Test:  [312/313]  eta: 0:00:00  model_time: 0.1296 (0.1303)  evaluator_time: 0.0194 (0.0260)  time: 0.1596  data: 0.0064  max mem: 9052\n",
      "Test: Total time: 0:00:53 (0.1702 s / it)\n",
      "Averaged stats: model_time: 0.1296 (0.1303)  evaluator_time: 0.0194 (0.0260)\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "IoU metric: bbox\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.346\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.640\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.280\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.152\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.411\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.530\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.273\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.345\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.360\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.186\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.412\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.530\n"
     ]
    }
   ],
   "source": [
    "# 使用混合精度训练\n",
    "scaler = torch.amp.GradScaler()\n",
    "max_epochs = int(max_iterations / (len(train_dataset) / train_batch_size))\n",
    "print(f\"Max epochs: {max_epochs}\")\n",
    "for epoch in range(max_epochs):\n",
    "    train_one_epoch(\n",
    "        model, optimizer, scheduler, train_dataloader, DEVICE, epoch, 100, scaler\n",
    "    )\n",
    "    # evaluate after every epoch\n",
    "    evaluate(model, val_dataloader, device=DEVICE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d88aba84",
   "metadata": {},
   "source": [
    "# Run inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cff4b15c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tf/anaconda3/envs/pytorch-cu124/lib/python3.11/site-packages/torchvision/utils.py:225: UserWarning: Argument 'font_size' will be ignored since 'font' is not set.\n",
      "  warnings.warn(\"Argument 'font_size' will be ignored since 'font' is not set.\")\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=482x640>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torchvision.io.image import decode_image\n",
    "from torchvision.utils import draw_bounding_boxes\n",
    "from torchvision.models.detection import (\n",
    "    FasterRCNN_ResNet50_FPN_V2_Weights,\n",
    ")\n",
    "from torchvision.transforms.functional import to_pil_image\n",
    "\n",
    "raw_img = decode_image(\"./resources/coco_test.jpg\")\n",
    "\n",
    "model.eval()\n",
    "\n",
    "# Step 3: Apply inference preprocessing transforms\n",
    "img = val_transforms(raw_img, None)\n",
    "img = [img[0].to(DEVICE)]\n",
    "# Step 4: Use the model and visualize the predictions\n",
    "prediction = model(img)[0]\n",
    "\n",
    "# get labels for each prediction\n",
    "indices = torch.where(prediction[\"scores\"] > 0.3)\n",
    "labels = [\n",
    "    FasterRCNN_ResNet50_FPN_V2_Weights.DEFAULT.meta[\"categories\"][i]\n",
    "    for i in prediction[\"labels\"][indices]\n",
    "]\n",
    "\n",
    "box = draw_bounding_boxes(\n",
    "    raw_img,\n",
    "    boxes=prediction[\"boxes\"][indices],\n",
    "    labels=labels,\n",
    "    colors=\"red\",\n",
    "    width=4,\n",
    "    font_size=30,\n",
    ")\n",
    "to_pil_image(box.detach())"
   ]
  }
 ],
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